r«^
indicators
in U. S.
I
areas, 1970
A COMPREHENSIVE ASSESSMENT
U. S. ENVIRONMENTAL PROTTKCTnON AGENCT
Washington Environmental Reieitrch Center
Washington, D,C. 20460 <
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OOOR75004
QUALITY OF LIFE INDICATORS
IN U.S. METROPOLITAN AREAS, 1970
A COMPREHENSIVE ASSESSMENT
By
Ben-Chieh Liu, Ph.D.
Grant No. R803049-01-0
(Program Element 1HA098)
Project Officer
Robert C. Livingston
Implementation & Methods Analysis Staff
Washington Environmental Research Center
Washington, D.C. 20460
Prepared For:
Washington Environmental Research Center
U.S. Environmental Protection Agency
Washington, D.C. 20460
May 7, 1975
The methodology and views expressed in the following
material do not necessarily reflect those held by the
Environmental Protection Agency (EPA) or by the Washington
Environmental Research Center (WERC), nor does
mention of tradenames or commercial products
constitute endorsement or recommendations for use.
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CONTENTS
Page
Abstract v
List of Panels, Figures, and Charts vi
List of Tables ix
Acknowledgements x*
Chapters
I Introduction 1
II Quality of Life Indicators:
A Review of the State of the Art 5
Conceptual Development 5
Specific Models of Social Indicators 12
Economic Models 13
Psychological Models 14
Environmental Models 17
Political Models 19
Sociological Models 23
Quality of Life Models 27
Quality of Life Models in the U.S. 27
Quality of Life Models in the Rest of the World 31
ii
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CONTENTS (Continued)
III Economics in Contemporary Society 36
Welfare Economics and the Quality of Life 36
A Production Approach to Quality of Life 39
IV Measuring the Quality of Life in Metropolitan Areas 52
Selection and Groups of Metropolitan Areas 52
The Quality of Life Factors and Data Sources 53
Economic Component 54
Political Component 58
Environmental Component 62
Health and Education Component 66
Social Component 69
Indicator Construction and Rating System Development 79
Method 1 83
Method 2 87
Method 3 88
V Quality of Life Findngs and Implications:
Large Metropolitan Areas (L) 93
Economic Component 93
Political Component 103
Environmental Component 111
Health and Education Component 119
Social Component 128
Summary and Conclusions 136
VI Quality of Life Findings and Implications:
Medium Metropolitan Areas (M) 139
Economic Component 139
Political Component 145
Environmental Component 151
Health and Education Component 158
Social Component 163
Summary and Conclusions 169
iii
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CONTENTS (Concluded)
VII Quality of Life Findings and Implications:
Small Metropolitan Areas (S) 172
Economic Component 172
Political Component 178
Environmental Component 185
Health and Education Component 191
Social Component 198
Summary and Conclusions 205
VIII Summary and Conclusions 208
Appendix - General Information 229
References 292
Author Index 307
iv
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ABSTRACT
The primary objective of this study is to quantitatively assess the
urban quality of life (QOL) and to analyze the variations in QOL
components in the 243 SMSA's in the U.S.
This study, based on a QOL production model, developed a systematic
methodology for constructing economic, political, environmental, health
and education, and social indicators to reflect the overall "health"
of the nation and its citizens' well-being. These five QOL components
consist of some 123 factors which were selected to reflect the essential
physical inputs in the QOL. Primary and secondary statistical data
for 1970 were collected, reorganized and modified to represent the 123
QOL factor inputs employed in the model to derive the QOL component
indexes.
For analytical purposes, the 243 SMSA's were divided into three popula-
tion groups--65 large SMSA's (with population larger than 500,000);
83 medium SMSA's (200,000 to 500,000); and 95 small SMSA's (less than
200,000). The SMSA's in each population group were rated outstanding
(A), excellent (B), good (C), adequate (D), or substandard (E) separately
for each component on the basis of their QOL index values relative to
the respective group means. A static, descriptive analysis of the
empirical results was performed, and important findings and relevant
policy implications were delineated.
There is clearly a need in our transitional society to define and to
identify the factors that determine and influence our general welfare.
It is essential, in brief, to construct a mechanism which can help us
to distinguish better from worse. Social Indicators 1973. published
by the Office of Management and Budget, analyzed trends in social factors
at the national level. This study of quality of life in all metropolitan
areas, along with previous studies for the states, provide for the first
time a comprehensive, static cross-section analysis at the subnational
level.
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PANELS
No. Chapter IV Page
1 Factors in Economic Component 57
2 Factors in Political Component 60
3 Factors in Environmental Component 64
4 Factors in Health and Education Component 68
5 Factors in Social Component 75
FIGURES
Chapter V
1 Geographic Distribution of Ratings:
Economic Component (L) 96
2 Geographic Distribution of Ratings:
Political Component (L) 109
3 Geographic Distribution of Ratings:
Environmental Component (L) 117
4 Geographic Distribution of Ratings:
Health and Education Component (L) 126
5 Geographic Distribution of Ratings:
Social Component (L) 135
Chapter VI
6 Geographic Distribution of Ratings:
Economic Component (M) 142
7 Geographic Distribution of Ratings:
Political Component (M) 148
8 Geographic Distribution of Ratings:
Environmental Component (M) 157
9 Geographic Distribution of Ratings:
Health and Education Component (M) 160
vi
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FIGURES (Continued)
No. Page
10 Geographic Distribution of Ratings:
Social Component 168
Chapter VII
11 Geographic Distribution of Ratings:
Economic Component (S) 176
12 Geographic Distribution of Ratings:
Political Component (S) 184
13 Geographic Distribution of Ratings:
Environmental Component (S) 190
14 Geographic Distribution of Ratings:
Health and Education Component (S) 197
15 Geographic Distribution of Ratings:
Social Component (S) 201
Chapter VIII
16 Geographic Distribution of Ratings:
Overall Quality of Life (L) 224
17 Geographic Distribution of Ratings:
Overall Quality of Life (M) 225
18 Geographic Distribution of Ratings:
Overall Quality of Life (S) 226
CHA.RTS
Chapter V
Regional Variations in Indexes:
Economic Component (L) 102
Regional Variations in Indexes:
Political Component (L) 106
Regional Variations in Indexes:
Environmental Component (L) 118
Regional Variations in Indexes:
Health and Education Component (L) 125
Regional Variations in Indexes:
Social Component (L) 132
vii
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CHARTS (Concluded)
No. Chapter VI Page
6 Regional Variations in Indexes:
Economic Component (M) 144
7 Regional Variations in Indexes:
Political Component (M) 150
8 Regional Variations in Indexes:
Environmental Component (M) 155
9 Regional Variations in Indexes:
Health and Education Component (M) 161
10 Regional Variations in Indexes:
Social Component (M) 167
Chapter VII
11 Regional Variations in Indexes:
Economic Component (S) 177
12 Regional Variations in Indexes:
Political Component (S) 183
13 Regional Variations in Indexes:
Environmental Component (S) 189
14 Regional Variations in Indexes:
Health and Education Component (S) 196
15 Regional Variations in Indexes:
Social Component (S) 203
Appendix
1 Data Sources - Economic Component 284
2 Data Sources - Political Component 285
3 Data Sources - Environmental Component 286
4 Data Sources - Health and Education Component 287
5 Data Sources - Social Component 288
viii
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TABLES
No. Chapter V Page
1 Index and Ratings of Economic Component (L) 95
2 Index and Ratings of Political Component (L) 105
3 Index and Ratings of Environmental Component (L) 114
4 Index and Ratings of Health and Education Component (L) 122
5 Index and Ratings of Social Component (L) 130
Chapter VI
6 Index and Ratings of Economic Component (M) 140
7 Index and Ratings of Political Component (M) 146
8 Index and Ratings of Environmental Component (M) 153
9 Index and Ratings of Health and Education Component (M) 159
10 Index and Ratings of Social Component (M) 165
Chapter VII
11 Index and Ratings of Economic Component (S) 173
12 Index and Ratings of Political Component (S) 179
13 Index and Ratings of Environmental Component (S) 186
14 Index and Ratings of Health and Education Component (S) 193
15 Index and Ratings of Social Component (S) 199
Chapter VIII
16 Quality of Life Indexes and Ratings in Large SMSA's 212
17 Quality of Life Indexes and Ratings in Medium SMSA's 214
18 Quality of Life Indexes and Ratings in Small SMSA's 216
ix
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TABLES (Concluded)
No. Appendix
A-l Basic Statistics of Economic Component (L) 232
A-2 Basic Statistics of Political Component (L) 234
A-3 Basic Statistics of Environmental Component (L) 236
A-4 Basic Statistics of Health and Education Component (L) 238
A-5 Basic Statistics of Social Component (L) 240
B-l Basic Statistics of Economic Component (M) 246
B-2 Basic Statistics of Political Component (M) 248
B-3 Basic Statistics of Environmental Component (M) 250
B-4 Basic Statistics of Health and Education Component (M) 252
B-5 Basic Statistics of Social Component (M) 254
C-l Basic Statistics of Economic Component (S) 260
C-2 Basic Statistics of Political Component (S) 264
C-3 Basic Statistics of Environmental Component (S) 268
C-4 Basic Statistics of Health and Education Component (S) 270
C-5 Basic Statistics of Social Component (S) 274
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ACKNOWLEDGEMENTS
Ihe author wishes to express his appreciation to the U.S. Environmental
Protection Agency which provided the grant making it possible to under-
take this study. The project monitors, Mr. Robert Livingston and
Dr. Peter House of Washington Environmental Research Center, EPA,
have been extremely cooperative during the entire course of the work.
In addition, many suggestions from Mr. Livingston also have resulted
in significant improvement in the study.
Messrs. Bruce Macy and Robert Gustafson assisted in theoretical
development and made important contributions to the project.
Miss Mary Kies capably and efficiently conducted the most painstaking
work of data collection, organization, and presentation, and
Mr. Raymond Posch developed all computer programs for index construction.
In addition to those of my colleagues at Midwest Research Institute
who worked with me, I am deeply indebted to Dr. Charles Kimball,
Messrs. John McKelvey, Gary Nuss, Robert Roberts, and Tom Ventresca
and Mrs. Mary Lillis for their encouragement during this research
effort as well as during the previous state quality of life study.
Valuable comments on technical matters from Dr. Murray Aborn of National
Science Foundation, Dr. Teh-wei Hu of Pennsylvania State University,
Drs. Maw-lin Lee and Ross Shepherd of the University of Missouri, and
Drs. Angus Campbell, Wilbur Cohen, and Daniel Tunstall of the
University of Michigan are gratefully acknowledged. I have also
benefited greatly from frequent discussions and information exchanges
with Dr. Clark Abt of Abt Associates, Dr. Michael Flax of Urban
Institute, Dr. Robert Foster of the Governmental Studies Programme,
Dalhousie University, Dr. 0. W. Markley of Stanford Research Institute,
Dr. Alex Micheles of the University of Guelph, Dr. Friedhelm Gehrmann
of the University of Augsburg, Dr. Robert Parke of the Center for
Coordination of Research on Social Indicators and Dr. Charles Wolf
of the Water Resources Institute, U.S. Army Corps of Engineers.
The U.S. Bureau of Outdoor Recreation and hundreds of Chambers of
Commerce provided us the first-hand information on recreation, sports
and cultural activities, and other agencies such as EPA and National
Weather Records Center supplied essential pollution and meterological
data.
xi
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It is impossible to name all individuals who have made important con-
tributions to this study. Nevertheless, the author owes special grati-
tude to thousands of readers and reporters throughout the world who,
for various purposes, used and commented on the state study which, in
turn, resulted in the improvement of the metropolitan study.
Appreciation also goes to Dr. Harold Orel and Mrs. Doris Nagel, who
devoted their time to editing the manuscript and to Mrs. Sharon
Wolverton and Mrs. Marsha Brown, who efficiently arranged the typing
and reproduction of the report.
Finally, the author has to thank his wife, Jill, for her endurance and
patience, for lonely weekends and late dinners during the preparation
of this study.
xii
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CHAPTER I
INTRODUCTION
A century ago John Ruskin seriously criticized the political economists
of his time for their preoccupation with material growth and neglect
of human values. During the Great Depression, the most influential
economist of this century, John Maynard Keynes, perceived the problems
of economic motivation, suggested that some appropriate preparations
for our destiny and for changes in our value system be made, and that
the arts of life be encouraged and experimented with, wealth serving
as a means rather than an end.— In his book The Affluent Society,
John K. Galbraith warned us that "In large areas of economic affairs
the march of events, above all the increase in our wealth and popular
well-being, has again left the conventional wisdom sadly obsolete."—'
In a recent work on world dynamics, Jay Forrester suggests that we
may just have passed through a golden age, and that our quality of
life may decline from what it was in the 1960's for the next century
or so.—' In 1972, a team of systems analysts at M.I.T. concluded that
if the present growth trends in world population, industrialization,
pollution, food production, and resource depletion continue unchanged,
the limits to growth on this planet will be reached within the next
century.A/
The U.S. society has certainly passed through an industrialization
era and seems to be in a great transition period toward a postindus-
trial stage. Uncertainty and confusion have rolled across the U.S.,
and a discontent with the quality of life seems to have been growing
JL/ See John Maynard Keynes, Essays in Persuasion (London: Macmillan
and Co., 1933).
21 John K. Galbraith, The Affluent Society (Boston: Houghton Mifflin
Co., 1958).
J3/ Jay Forrester, Urban Dynamics (Cambridge: The M.I.T. Press, 1969)
4/ D. H. Meadows, D. L. Meadows, J. Randers, and W. W. Behrens III, The
Limits to Growth (New York: Universe Books, 1972).
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faster than technological know-how and material wealth in this country.
They have developed as a result of conflicting values: "operative
values" in the industrial state and the "declared values" important in
the founding of our nation. While the former is characterized by the
competitive factor, the division of labor, indefinite economic persua-
sion, the use of the scientific method and technological advances the
latter is highlighted by concerns with equality, justice, and natural
rights such as life, liberty, and the pursuit of happiness.
In an industrial society, individuals struggle for survival with very
limited time for leisure; hard work is a virtue, and wealth accumula-
tion becomes the status symbol or the ultimate goal of the hard work.
The great transition period—which leaves more time for thinking and
leisure--makes it possible for people to move beyond their basic
concerns of living to a humanistic concern for what living is all about.
As John Rockefeller III pointed out in The Second American Revolution,
the latter concern embodies a desire to create a human-centered society,
and to harness the forces of economic and technological advancement in
the service of humanistic values. In other words, people in the
transitional period may be characterized by a devotion to human welfare,
and an interest in all human beings.— However, at the beginning of
this period, people are puzzled about which path to follow as they
search for a doctrine, set of attitudes, or a way of life centered
upon human interests or values. The ultimate goal of the search is
obviously to reach a society such as the Ta-Tong characterized by
Confucius--a state of enduring wholeness and beauty in which an
individual may identify himself and contribute his best to other men,
to society, to nature, and to the land in exchange for a meaningful,
happy, and satisfactory life.
In seeking ways to move our society from an industrial state toward a
humanistic-oriented psychology that seeks to improve the quality of
life of all Americans, the role of the government as a leader, as well
as a servant, must be considered. In addition to the necessary duty
of protecting international status and security and striving for
economic growth and full employment with stable prices, the Federal
Government is already beginning to manage social changes: civil rights
legislation, income redistribution, environmental protection and
problems involved with urbanization and population growth, etc. State
and local governments are also increasingly concerned about the social
problems of organized crime, urban renewal, mass transit, welfare
5/ John Rockefeller, III, The Second American Revolution (New York:
Harper and Row, 1973).
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provisions, community beautification, etc. To be specific, our
Government is more aware of the change in social values than ever
before and seeks to solve the problems in order to improve the
national health and overall social well-being.
However, a problem is not likely to be solved until it has been per-
ceived and identified as a problem. Although there exist thousands
of decision makers within the private sector who are able, willing,
and devoted to the enhancement of our overall quality of life, they
are not certain about the direction that their philanthropical activi-
ties should take, just as many public decision makers are not always
sure about the social, economic, political and environmental impacts
of their actions.
In order to promote the general welfare, there is an urgent need in
our transitional society to define the general welfare and to identify
the factors that determine and influence our general welfare. In
brief, it is essential to construct a mechanism which can distinguish
better from worse. "For many of the important topics on which social
critics blithely pass judgments, and on which policies are made,"
said Bauer, "there are not yardsticks by which to know if things are
getting better or worse."£/ As it now stands, the United States has
no comprehensive set of social statistics that reflect our changes in
values and measure social progress or retrogression.—' One of the
most detrimental features of the social sciences to date has been the
absence of any generally acceptable condensed set either of social
welfare functions or of social conditions.
The search for quality of life indicators is an attempt to obtain new
information that will be useful to evaluate the past, guide the action
of the present, and plan for the future. The empirical measures of
various levels of quality of life enjoyed by Americans are aimed at
the identification of strengths and weaknesses of our national health
so that decision makers, be they public or private, can be assisted
as they seek to evaluate, guide, and plan for a better quality of life.
6/ Raymond Bauer (ed.), Social Indicators (Cambridge: The M.I.T.
Press, 1966) p. 20.
2.1 See National Goals Research Staff, Report to the President. Wash-
ington, D.C., 1970.
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The study, The Quality of Life in the U.S.. 1970. at the state level,
and this study for all metropolitan areas, represent exploratory
efforts to meet these needs.—'
In the following text, we first review the state of the art of
research efforts in the field of quality of life measurement. The
relationship between welfare economics and the quality of life and a
production model for quality of life are discussed in Chapter III.
Chapter IV deals with the scope, methodology and data sources of the
empirical quality of life study for all 243 standard metropolitan
statistical areas. Empirical findings based primarily on 1970 data and
policy implication are presented in Chapters V, VI, and VII, respectively,
for the three groups of SMSA's--large, medium, and small. Finally, a
summary and suggestions for future research are contained in the last
chapter.
Ben-Chieh Liu, Quality of Life in the U.S.. 1970 (Kansas City:
Midwest Research Institute, 1973).
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CHAPTER II
QUALITY OF LIFE INDICATORS:
A REVIEW OF THE STATE OF THE ART
This chapter presents an extensive review of the quality of life indi-
cator development throughout the world. Discussion will first be on
the conceptual development and, secondly, the specific models of
social indicators. The last part of this chapter will focus on the
general quality of life models. It is hoped that this review will
provide useful information and guidance for future research in the
field.
CONCEPTUAL DEVELOPMENT
Over the last decade, an era that does not coincide particularly with
any specific political administration, this nation has witnessed an
erosion of the consensus about our socioeconomic system. It has been
a period in which real incomes grew unusually rapidly, yet the dissat-
isfaction with our social order and system was both overwhelming and
unprecedented. Is economic growth really associated with some subtle
forces which reduce social well-being in some dimensions, just as they
improve it in others? Do the obvious manifestations of discontent in
a rapid income-growing and highly affluent society simply misrepresent
a general increase in contentment, or are there some people who have
been made worse off as a consequence of economic growth? Why should
new technology and a high rate of income growth fail to diminish social
pathology and improve the overall quality of life?
Economic growth requires capital accumulation, technological change,
and improvement in human skills. In modern times, it also often
requires changes in institutional structure and resource location.—'
As a result, generally desirable economic growth may frequently be
associated with undesirable social and environmental costs.
\l For a variety of discussions on economic growth or no growth society,
see Daedalus, Journal of the American Academy of Arts and Sciences
(Fall, 1973).
5
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Economic growth, no matter how measured or in which sector, tends to
increase the production of unwanted by-products--urban traffic conges-
tion and time spent on the roads; air, water and other types of pol-
lution; social disorder and tension; housing problems and unequal dis-
tribution of incomes; loosening of family ties and friendships, etc.
When the costs of the by-products become greater than the economic
gains, societal discontent becomes unavoidable and the overall
quality of life degraded for most of the people.—'
The effects of economic growth on our overall welfare or on the quality
of life are inextricably intertwined, but arguments for and against
economic growth are largely subjective. As concern over the quality
of the environment and social welfare mounts, the conventional
measure of well-being, GNP, which has served for decades as a means of
establishing goals and measuring achievement of the goals at the
policy-making level, has been criticized—on the one hand—because it
is not an appropriate index of welfare, and—on the other—because it
does not include the important values of increased leisure, the
services of housewives, the hidden rent, farmer's consumption of
their own products, etc. Governments, like private researchers, have
become more concerned with improving both the economic and social
performance of society. Beyond providing for employment and price
stability, law and order, and national defense, governments are recog-
nizing that they must involve themselves with a wide variety of social
conditions which affect our quality of life such as the health of the
population; equal opportunity among individuals; the eradication of
poverty and discrimination; more security for the aged; more equal
distribution of incomes; urban housing; transportation; and pollution
problems, etc.—'
The quality of life concept or the social indicator movement has been
a response to these needs for information on social conditions related
Most notable arguments of these can be found in D. H. and D. L.
Meadows, J. Randers and W. W. Behrens III, The Limits to Growth
(New York: Universe Books, 1972); E. J. Mishan, The Costs of
Economic Growth (New York, 1967).
For instance, see R. Cole, Errors in Provisional Estimates of Gross
National Product (New York: National Bureau of Economic Research,
1969); N. Ruggles and R. Ruggles, The Design of Economic Accounts
(New York, 1970); W. Nordhaus and J. Tobin, "Is Growth Obsolete,"
in Economic Growth, 50th Anniversary Colloquium V (New York); and
a section on "Social Indicators and a Framework for Social and
Economic Accounts," 1974 Proceedings of the Social Statistics
Section, American Statistical Association.
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to a variety of dimensions of the national welfare beyond such economic
measures as real income per capita. This movement is generally said
to have begun in 1929, with President Hoover's Committee on Social
Trends. That Committee's report, Recent Social Trends in the United
States (1933), was an attempt to analyze social factors likely to have
a bearing on public policy in the second third of the century.
However, very little progress was made in regular social reporting
until 1960. A variety of national goals on the social front were set
up by President Eisenhower's Commission on National Goals in 1960.
In 1962, the Social Science Advisory Committee (to President Kennedy)
urged the establishment of a systematic collection of basic behavioral
data for the U.S. The National Commission on Technology Automation
and Economic Progress, in 1966, called for social accounting, annual
social reports to the President, and a full opportunity and social
accounting act.—'
Methodological development of social indicators and interest in the
quality of life concept development grew remarkably during the later
years of the 1960's. Following the studies on social indicators by
Bauer (1966), and Sheldon and Moore (1968), Wilbur Cohen, Secretary of
HEW, proposed in 1968, establishment of a Council of Social Advisors
to analyze the quality of life in the U.S.^ The President's Commission
on Federal Statistics also accepted the challenge to improve the
quality of federal statistics in the 1970's, and new developments in
labor statistics, such as employment safety and working conditions,
are already underway at the Bureau of Labor Statistics.— The U.S.
Environmental Protection Agency (EPA) also made an effort to improve
the tools available to decision makers who are necessarily involved in
the quality of life production and delivery systems. A large-scale
^/ See the Report of the President's Commission on National Goals,
Goals for Americans (Englewood Cliffs, New Jersey: Prentice Hall,
1960), and for further information see Environmental Protection
Agency, The Quality of Life Concept (Washington, D.C.: U.S.
Government Printing Office, 1973), pp. 1-10.
J>/ See Raymond B. Bauer (ed.) Social Indicators (Cambridge: M.I.T.
Press, 1966), and Eleanor Sheldon and Wilbert Moore, Indicators
of Social Change; Concepts and Measurements (New York: Russell
Sage Foundation, 1968), and Wilbur Cohn, Toward a Social Report
(Washington, B.C.: U.S. Government Printing Office, 1969) and
The Quality of Life and Social Indicators (New York: National
Bureau of Economic Research, 1972).
6/ See W. Moore and S. Maxine, "New Development in Labor Statistics,"
Monthly Labor Review (March 1972), pp. 3-13.
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symposium on the subject, "The Quality of Life Concept--A Potential
New Tool for Decision Makers," was sponsored by. EPA in 1972, which set
another significant milestone for quality of life research and the
social indicator movement.— Two years later, the Office of Manage-
ment and Budget published Social Indicators, 1973, a book of statistics
selected and organized to describe social conditions and trends in
the U.S. and the first of its kind to be published by the Federal
Government.—/ Studies such as this present study have been recently
supported by federal funds.
Although it is generally understood that the need for quality of life or
other social indicators is urgent because they are essential to assessment
of many aspects of social progress and social accounting, and are useful
for national goal setting, project planning, priority ranking, program
manipulation, and performance evaluation, there is no consensus as to what
the quality of life is all about, and how the quality of life or other
social indicators should be defined, for whom, and in what manner they
should be constructed. This failure to reach a consensus can be sub-
stantially attributed to the absence of a commonly accepted social wel-
fare function or value system.
The U.S. Department of Health, Education and Welfare, in Toward A
Social Report, defines social indicators as follows:
A social indicator--may be defined to be a statistic of direct
normative interest which facilitates concise, comprehensive
and balanced judgments about the condition of major aspects
of a society. It is in all cases a direct measure of welfare
and is s"bject to the interpretation that, if it changes in
the "right" direction, while other things remain equal,
things have gotten better or people are "better off."—
The key concepts here are "normative interest" which implies that
social indicators must be those with which the majority of our people
are directly concerned; their changes can normally be properly inter-
preted. Perloff notes that indicators are "normally used to describe
the condition of a single element, factor, or the like., which is part
T_l The results of the symposium were published in Environmental Pro-
tection Agency, The Quality of Life Concept (Washington, D.C.:
The Government Printing Office, 1973).
B/ Daniel B. Tunstall, Social Indicators, 1973 (Washington, D.C.:
Office of Management and Budget, 1974).
j)/ U.S. Department of Health, Education and Welfare, Toward a Social
Report (Washington, D.C.: U.S. Government Printing Office, 1969),
p. 97.
8
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of a complex, interrelated system." Sheldon and Freedman state that
"social indicators are time series that allow comparisons over an
extended period which permit one to grasp long-term trends as well as
unusually sharp fluctuation rates." -' The emphasis is thus changing
from the normative interest to positive, time series observation,
and predictions.
Land states that social indicators should be the constituent parts of
some social model or theory about how society operates. Olson views
them as part of a coherent system of socioeconomic measurement which
can facilitate comprehensive and balanced judgment about the condition
of major aspects of a society. Sawhill describes social indicators
as quantitative measures of social conditions designed to guide choices
at several levels of decision making. According to Smith, their
compilation and use should be related to public goals. For these
definitions social indicators are considered as strategical variables
included in a model which enables decision makers to make efficient
and effective policies concerning social well-being.—
"Quality of Life" is a new name for the older terms "general welfare"
or "social well-being." The preamble to the U.S. Constitution includes
as one statement of purpose, "to promote the general welfare." The
National Environmental Policy Act mandates the Federal Government to
10/ Harvey Perloff, "A Framework for Dealing with Urban Environment:
Introductory Statement," in Harvey Perloff (ed.), The Quality
of the Urban Environment (Washington, D.C.: Resources for the
Future, Inc., 1969); Eleanor Sheldon and Howard Freedman, "Notes
on Social Indicators: Promises and Potential," Policy Sciences
I (1970), p. 97.
ll/ See Kenneth C. Land, "Social Indicators," in R. B. Smith (ed.)
Social Science Methods (New York: The Free Press, 1970); and
"On the Definition of Social Indicators," American Sociology
(November 1971), pp. 322-325; M. Olson, "Social Indicators and
Social Accounts," Socioeconomic Planning Sciences, 2 (1969),
pp. 335-346; I. V. Sawhill, "The Role of Social Indicators and
Social Reporting in Public Expenditure Decisions," in The Analysis
and Evaluation of Public Expenditures: The System, papers sub-
mitted to the Joint Economic Committee of the U.S. Congress
(Washington, D.C.: U.S. Government Printing Office, 1969);
and David Smith, The Geography of Social Well-Being in the U.S.
(New York: McGraw-Hill, 1973), p. 54.
-------
take action "...in protecting and enhancing the quality of the Nation's
environment to sustain and enrich human life." Most people approach
quality of life with widely preconceived definitions which vary sub-
stantially with respect to time, place, and the individual. In the
study, Pattern of Human Concerns. for example, Cantril found that most
U.S. people in 1959 were first concerned about their own health and
a decent standard of living; concerns about children, housing, happy
family, and family health surpass other categories. With respect to
the concerns people had for this country, almost one-half of the
respondents wanted peace. Next to that were an improved standard of
living (14 percent), employment (13 percent), economic stability
(12 percent), and international cooperation (12 percent). Although
a similar, personal preference picture of individual concerns was
revealed in West Germany in 1957, the general categories of hopes for
the nation were substantially different. That country's reunification
ranked as first priority (44 percent), peace and economic stability
stood high (37 percent and 24 percent, respectively), and next came
1 9 /
standards of living and employment.—'
In contrast, the national problems in the U.S. of greatest concern in
1973 were significantly different in nature and magnitude from those
in 1959. Newsweek reported that inflation (64 percent) and lack of
integrity in government (43 percent) became the most urgent concerns
in the country in 1973. Next on the list were crime, welfare, federal
spending, taxes, pollution, overpopulation, and energy shortage—each
of them had more than 10 percent of the votes.—' A recent survey
revealed that although many Germans are puzzled by the expression,
"Quality of Life," the majority of them still relate it to issues such
as an improved standard of living, a pleasant, secure life, a demand
for environmental protection, and some satisfactory love life.—
There are as many quality of life definitions as there are people.
The following may serve as a sample of the variety. While Perloff
considers quality of life as elements or accounts of comprehensive
systems of data characterized by a balance between inputs and outputs
or inflows and outflows, or providing the value of the total stock of
various times in a total system, Whitman developed a complex quality
12/ See Hadley Cantril, The Pattern of Human Concerns (New Jersey:
Rutgers University Press, 1965).
I3f See "What America Thinks of Itself," Newsweek (December 10, 1973).
14/ See U.S. Department of Housing and Urban Development, International
Information Series, 26 (February 5, 1974), p. 6.
10
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of life system—an environmental evaluation system, which is said to
be replicable, analytical, and comprehensive, broad enough to include
all relevant types of environmental measurements and indicators as
determined through an interdisciplinary perspective. Hornback and
Shaw define "Quality of Life" as a function of the objective conditions
appropriate to a selected population and the subjective attitude toward
those conditions held by persons in that population. Dalkey and Rourke
think that by "Quality of Life" is meant a person's sense of well-
being, his satisfaction or dissatisfaction with life, or happiness
or unhappiness. Christakis and Terleckyz approach the quality of life
definition through social goals and policy formulation, and they
specify and examine a multidimensional entity of many quality of life
components between the desired and the actual levels.—'
Wingo and Liu, in a microeconomic framework, suggest that quality of
life may be reflected jointly in two dimensions: (1) the income or
wealth which represents command over physical resources and is trans-
ferable, and (2) the psychological inputs which are personal, non-
transferable, and related to the intensity of private, subjective
gratifications. However, while Wingo employs a utility maximization
concept, Liu employs an individual production approach in which each
individual is supposed to optimize his own level of quality of life.—'
15/ Harvey Perloff, pp. pit.; Ira Whitman et al., Design of an Envi-
ronmental Evaluation System (Columbus, Ohio: Battelle Columbus
Laboratories, June 1971); Kenneth Hornback and Robert Shaw, Jr.,
"Toward a Quantitative Measure of the Quality of Life" in
Environmental Protection Agency, The Quality of Life Concept,
op. city, Norman Dalkey and Daniel Rourke, "The Delphi Procedure
and Rating Quality of Life Factors," in Experimental Assessment
of Delphi Procedures with Group Value Judgments (California:
Rand Corporation, 1971); Alexander Christakis, "Limits of Systems
Analysis of Economic and Social Development Planning," Existies
200 (July 1972); and Nestor Terleckyz, "Measuring Progress
Towards Social Goals: Some Possibilities at National and Local
Levels," Management Science (Volume 16, Number 12, August 1970).
16/ Lowdon Wingo, "The Quality of Life: Toward a Microeconomic
Definition," Urban Studies (October 1973); and Ben-Chieh Liu,
"Variations in the Quality of Life in the U.S. by State, 1970,"
Review of Social Economy (Volume XXXII, Number 2, October 1974)
and "Quality of Life: Concept, Measure and Results," The Ameri-
can Journal of Economics and Sociology (Volume 34, Number 1,
January 1975).
11
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The quality of life concept has become a focal point of converging
social, economic, political, and environmental considerations. Serious
attempts are being made to develop the concept into a useful tool for
decision makers in the public and private sectors. Although the con-
cept of quality of life can be described in various forms, depending
upon one's perspective, location, and time, it is no doubt a multi-
dimensional interdisciplinary subject. The overall development of the
quality of life concept may be generally summarized in the following
models:
1. Precise definitions of what constitutes quality of life, e.g.,
happiness, satisfaction, wealth, life style, etc.
2. Definition through the employment of a specific type of subjective
or objective social indicator, e.g., GNP, NEW, health or welfare indi-
cator, educational indicator, environmental, etc.
3. Indirect definition by specification of variables or factors
affecting the quality of life, e.g., a group of social, economic, po-
litical, and environmental indicators represented by different types
of composite indexes.
In this study, quality of life is defined as the output: of a certain
production function of two different but often interdependent input
categories—physical inputs which are objectively measurable and trans-
ferable, and the psychological inputs which are subjectively, ordinally
differentiable but usually not interpersonally comparable. The basic
assumption under this approach is that every rational individual always
attempts to optimize the level of his life-quality subject to his
capability constants in a given time and at a given place. To partially
quantify quality of life, the aggregate over time, it is necessary and
feasible at" the present stage to measure the changes in the physical
inputs over that period of time through some commonly agreed-on indexes.
SPECIFIC MODELS OF SOCIAL INDICATORS
Social indicators have been modeled by a number of major disciplines,
including economics, sociology, psychology, political science, and
environmental sciences. Each discipline has its own understanding of
how values and ideas should be defined and quantified. As a result,
the social indicator models cover a wide spectrum. A thorough review
of these models becomes an endless task. Nevertheless, an understanding
12
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of these various value perspectives will enable us to identify the
critical concerns regarding quality of life assessment.
Economic Models
From an economic perspective, since the ages of Copernicus and Descartes,
people's thoughts in the Western Hemisphere have been directed at a
mechanical universe which can be experienced and measured scientifically.
The 19th century economists, W. S. Jevons, Leon Walras, and Alfred
Marshall, building theories based on these concepts developed the
economic principle of the greatest good for the greatest number by
assuming that interpersonal utility is measurable. Individuals were
considered to possess cardinal utility, and it was assumed that human
nature is more complex than any simple summation of happiness and
dissatisfaction or pleasures and pains. Although later economists in
the ordinal utility school deserted the assumption that interpersonal
utility is comparable, they still require that a rational individual's
preferences be consistent and transitive, i.e., the more you have and
the higher you move to the right and on to another indifference curve,
the better. Consequently, economic growth in GNP or real income per
capita has been a dominating policy goal with near universal support
for the past 4 decades. In fact, Simon Kuznets, developer of the GNP
measure or the national income accounting system which sums the earnings
of the labor and property which are used to produce final goods and
services for a given period, won the Nobel Prize in economics.—'
The concept of economic indicators as instruments for predicting economic
fluctuations in the short run and for controlling business cycles in the
long run was nurtured by the Depression. Methodologically, normative
models probably have been partially replaced by the positive approach
in that concerns with social goals have been distinguished from purely
scientific predictions. The stress of positive economics has been on
technical analysis such as econometric simultaneous equation models,
input-output studies, linear (or mathematical) programming, game theory
and operation research (or simulation).!£/ Even the recently developed
17/ For his studies, see Simon Kuznets, National Product Since 1869
(New York: National Bureau of Economic Research, 1946); "Pro-
duction of Capital Formation to National Product," American
Economic Review, Volume 42 (May 1952), pp. 507-526.
187 Incidently, Wisely Leontief, the inventor of input-output model,
also won a Nobel Prize in Economics a couple of years ago.
13
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Measure of Economic Welfare (MEW) by Nordhaus and Tobin, which attempts
explicitly to take into account in the GNP measure the hitherto overlooked
values of goods and services not traded on the market, such as leisure,
and to exclude intermediate market traded items such as defense expendi-
tures, still leaves the knotty problems of human action and behavior
largely untouched.
Economic indicators have been the traditional principal measures of
overall national prosperity and social well-being. Not until recently
did the risks of economic growth and the social costs associated with
such growth call sufficient attention to the need for reexamination of
national goal setting and policy making .ii' There are likely to be
important changes in the existing national income accounting measures
that will move the national income accounting series closer to a com-
plete welfare measure. However, it seems ill-advised to change the
national product measurement of GNP to a comprehensive social welfare
measure. Efforts to do so, according to Denison, can only impair the
usefulness of GNP or other economic measures of both long- and short-
term economic analysis they now very well serve.—'
Psychological Models
In the attempt to construct social indicators, psychologists usually
approach them from a personal or individual perspective. Sir Isaiah
Berlin observed that there are deep differences in the way in which
people approach life. One approaches a problem in an integrative
manner, trying to bring everything into a single, universal organizing
principle that gives unity to the manifest diversities of life; another
may pursue disparate problems with little concern for how they are re-
lated and fit into a larger framework. According to Norman Bradbum,
the former group may be the pure theorists, and the latter, empiricists.
The split in the field of mental health between the two groups, as
pointed out by Bradbum, "has resulted in theories that dangerously
approach explaining everything, and thus explaining nothing, 9?,in
disparate empirical findings that do not add up to anything."—
19/ For interested readers, the controversial issues on growth are
presented in Daedalus. Journal of the American Academy of Arts
and Sciences (Fall 1973).
20/ See Edward Denison, "Welfare Measurement and the GNP," in Survey
of Current Business (January 1971).
21 / See Norman Bradbum, The Structure of Psychological Well-Being
(Chicago: Aldine Publishing Company), preface.
14
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In a new theory of behavior, H. J. Campbell shows that human thinking
and behaving, human personality, and the human system of value may be
marked by five different classes when we search for pleasure or happi-
ness, i.e., classes of the subhuman behavior, of the search for mul-
tiple pleasures, of the thinkers, of the human institutions and of the
human destiny.±±J When measuring the quality of life or social health,
it is, therefore, essential to clearly identify the classes and indi-
viduals for whom the indicators are developed. Angus Campbell and
Philip Conversee discuss quality of life from the standpoint of per-
sonal experience, i.e., aspiration, satisfaction, disappointment, and
frustration. They assume that satisfaction or frustration are ex-
00 /
periences that most people can report with reasonable validity.—'
Abraham Mas low approaches the perspective of individual needs and values
with five levels of "needs hierarchy." They are, in ascending order,
physiological (or survival); safety; belongingness and love; esteem;
and self-actualization. According to Maslow, there will be no more
development after one has arrived at the level of "self-actualization."
A recent theory developed by Graves, Huntley, and Bier describes the
eight-level open-ended indicators which not only explain that current
social turmoil is due to the transition process of moving from one
"need" to another, but can be applied to both individuals and organi-
zations as well. A person's or organization's level of satisfaction
can be discovered through the use of empirical survey.—' In
Sources of Satisfaction, Penelope and Maynard Shelly stressed that a
realistic study of the sources of man's satisfaction cannot ignore the
changes that are taking place during this great transition, and found
that the evolution of satisfaction shows progressive changes in three
components: genetic, personal, and social.—' The theoretical modeling
in the psychological field, thus, covers not only static and individual
well-being, but also dynamic, societal, and institutional elements.
22/ H. J. Campbell, The Pleasure Areas (New York: Delacorte Press, 1973)
23/ Angus Campbell and Philip Conversee, The Human Meaning of Social
Change (New York: Russell Sage Foundation, 1972).
24/ Abraham Maslow, Motivation and Personality (New York: Harper and
Row, 1970); and Clare Graves, W. Huntley and Douglas Bier,
"Personality Structure and Perceptual Readings: An Investiga-
tion of Their Relationship to Hypothesized Levels of Human
Existence," mimeographed paper, 1965.
25/ Penelope and Maynard Shelly, Sources of Satisfaction (Lawrence,
Kansas: The Key Press, 1973).
15
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Empirical studies on the subject are numerous. Scott utilized a three-
dimensional interdependent model of the self, the other, and the commu-
nity to measure happiness among children, high school students, univer-
sity students, and normal adults for a given point in time.—
In the attempt to discover from the point of view of the individual
participants in social and national life just what the dimensions and
qualities of this reality world were, Cantril investigated the pattern
of human concerns among countries, including indicators covering a
broad spectrum ranging from individual and family health, lob oppor-
tunity, and safety, to government and international peace.—' ln
measuring work satisfaction, Herzberg, Mansner, and Snyderman noted
the existence of two groups of factors: satisfiers and dissatisfiers.
Both played an important role in the work satisfaction level deter-
mination.—' Following them, Bradbum postulates a conceptual scheme
that describes psychological well-being as a function of two inde-
pendent dimensions--positive and negative effects—each of which is
related to well-being by an independent set of variables. When he
translated those concepts into operational measures and collected
systematic data for social, economic and demographic variables included
in his model, he found not only that the two types of positive and
negative factors are independent of one another, but also that "the
more one has, the more one gets." To those who have attributes that
go with positions higher in social structure, such as higher educa-
tion and income, also go the psychic rewards of greater happiness.—
In summary, psychological indicators are mostly subjective in nature,
and the scope of their measurement is still focused on personal or
individual well-being. The empirical work in this field can be con-
sidered a part of, but far from complete, measurement of overall social
well-being.
26/ Edward Scott, An Arena for Happiness (Springfield, Illinois:
Charles C. Thomas, 1971).
27/ See Hadley Cantril, The Patterns of Human Concerns (New Brunswick,
New Jersey: The Rutgers University Press, 1965).
28/ F. Herzberg, B. Mansner and B. Snyderman, The Motivation to Work
(New York: Wiley, 1959).
29/ See Norman Bradbum, op. cit., p. 226.
16
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Environmental Models
In the last few generations, mankind's propensity to change the envi-
ronment has accelerated. The power to use and adapt environment has
become concomitantly the power to destroy it abruptly. We have been
guided by the economic dogma that the common good emerges from the
competitive struggle of private interests. The public interest has
been neither expressed nor clarified and agreed upon. The national
wealth of human and nonhuman resources, as observed by ecologists, has
been converted into final products for consumption at a time when en-
vironmental conditions may have become so degraded as to render
extravagant consumption wasteful and environmental problems incurable.
As a result, The National Environmental Policy Act was enacted, and
the Council on Environmental Quality was authorized to promote the
development of indexes and monitory systems to determine the effec-
tiveness of programs for protecting and enhancing environmental
quality to sustain and enrich human life. A large number of environ-
mental impact statements for highway construction and resource develop-
ment projects have been produced.
Instruction and model specifications in measuring environmental quality
and impacts were given in the interim guidelines for implementing NEPA
in April 1970, by the Council on Environmental Quality. Subsequently,
the U.S. Department of Transportation and the U.S. Army Corps of
Engineers also issued guidelines for the preparation of environmental
impact statements which include analyses of social and economic indi-
cators in addition to the environmental indicators of possible project
impacts. Various impacts under conditions with and without the project,
plus differences among alternative projects, are required to be studied
prior to the construction. Wolf and others have studied these environ-
OQ/
mental impacts in detail.—'
One of the attempts to systematically relate project actions to envi-
ronmental condition changes can be found in the U.S. Geological Survey
30/ See C. P. Wolf, "Social Impact Assessment: The State of the Art,"
(Fort Belvoir, Virginia: Institute for Water Resources, U.S.
Army Corps, 1974); and John Kessler, "The Federal Highway Ad-
ministration," and Donald Lawyer, "The U.S. Army Corps of
Engineers," in Robert Ditton and Thomas Goodale (eds.), Envi-
ronmental Impact Analysis; Philosophy and Methods (Madison,
Wisconsin: University of Wisconsin Sea Grant Publication, 1972).
17
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Circular 645 by Leopold and others, and in the "Information System for
Environmental Planning" by Lyle and von Wodtke. They employed a matrix to
show the relation of a project's action activities to a listing of environ-
mental conditions that might be affected by the action activities.—/
This simple matrix model depicts the network of interrelationship
between an action and its consequent environmental effects.
The National Wildlife Federation has constructed Environmental Quality
Indexes since 1969. These indexes represent efforts designed to pro-
vide the concerned citizen with a comprehensive review of published
information on factors affecting environmental quality. The principal
variables considered in the model are soil, air, water, living space,
minerals, wildlife and timber. Furthermore, the Environmental Protec-
tion Agency has been generating a variety of air, water and solid waste,
and other environmental pollution indicators in the U.S., and the
Federal Department of the Environment in Canada has also developed a
National Environmental Quality Index for Canada.— In a description
of an environmental evaluation system, Whitman and his associates
simplify the environment into a relatively small number of measure-
ments and indicators that can be used to determine the project's impact
upon the environment. In the model, total environmental impacts are
evaluated through four levels of generality, namely, environmental
categories—ecology, pollution, aesthetics, and human interest; com-
ponents within each category; and parameters and measurements within
each component.—' Thomas proposes to identify and classify the
problems of environmental control for an animal farm on the basis of a
mathematical structure and the type of utility or disutility pertaining
31/ Luna Leopold, Danke Frank, Bruce Hanshaw and James Balsley, A
Procedure for Evaluating Environmental Impact (U.S. Department
of the Interior, Geological Survey Circular 645, 1971); John
Lyle and Mark von Wodtke, "Information System for Environmental
Planning," in Journal of the American Institute of Planners.
Volume 40, Number 6 (November 1974), pp. 394-413.
32/ Thomas Kimball, "Why Environmental Quality Indices," in Environ-
mental Protection Agency, The Quality of Life Concept (Washington,
D.C.: Government Printing Office, 1973); H. Inhaber, "Environ-
mental Quality: Outline for a National Index for Canada,"
Science. Volume 186, Number 4166 (29 November 1974), pp. 798-804.
33/ Ira Whitman et al., "A Description of An Environmental Evaluation
System," in EPA, op. cit.
18
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to people, such as longevity, health, safety, aesthetics, etc.-2-t'
Lave and Seskin employed a multiple regression model to study air
pollution impacts on human health with varying pollution indicators
among metropolitan areas, while Leontief analyzed the environmental
repercussions and the economic structure with an input-output model.—
Taking into consideration the mental images that men have of geographic
space, Gould tried to model and map psychological preferences onto
the geographic locations. Sonnenfeld, in another endeavor, attempted
to measure and account for variations in man's sensitivity to the
0£ /
environment among cultural groups. —'
Environmental models, in short, represent specific interests in natural
environments. Although they differ from economic and psychological
models in the specification of variables included, the methodology for
constructing component indicators is similar among these different
economic, psychological, and environmental models. Just as psychologi-
cal well-being cannot represent the overall national health, environ-
mental quality cannot fully reflect our life quality either.
Political Models
Following Easton, the subjective political orientations may be directed
toward three distinctive levels of the political system: the government,
the regime, and the political community.—' Each level may be regarded
as an object of orientation for elements of the political culture.
In a system form, Patterson developed a somewhat open-ended, multi-
faceted, sensitizing, political culture model to study the components
34/ Harold Thomas, Jr., "The Animal Farm: A Mathematical Model for
the Discussion of Social Standards for Control of the Environ-
ment," Quarterly Journal Economics (February 1963).
35/ Lester Lave and Eugene Seskin, "Air Pollution and Human Health,"
Science. Volume 169 (August 21, 1970); Wassily Leontief, "Envi-
ronmental Repercussions and the Economic Structure: An Input-
Output Approach," The Review of Economics and Statistics, Volume
52, Number 3 (August 1970).
36/ See Peter Gould, "On Mental Maps," and Joseph Sonnenfeld "Environ-
mental Perception and Adaptation Level in the Arctic," in David
Lowenthal (ed.), Environmental Perception and Behavior (Chicago:
Chicago University, Department of Geography, 1967).
37/ See David Easton, A System Analysis of Political Life (New York,
1965).
19
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of state political cultures which are often considered as determinants
of policy processes and outputs. In the model, he considered three
elements of political culture: empirical beliefs, expressive symbols,
and values for the evaluation of political efficiency, citizen duty,
One of the most interesting works in the political models may be the
Legislative Evaluation Study conducted by the Citizens Conference on
State Legislatures (CCSL) . The major tasks of the study are to
develop specific criteria for the evaluation of the technical capa-
bilities of the state legislatures and to collect data and, subse-
quently, rank state legislatures according to the specific criteria
selected in the study. The primary objectives of the study are:
* To focus the attention and concerns of members of the public and
legislators on many of the significant disabilities which limit
the effective performance of some state legislatures;
* To furnish diagnostic indicators of particular deficiencies in
particular states, and thus to give guidance to legislative efforts
toward legislative improvement;
* To provide benchmark documentation as a yardstick for measuring prog-
ress over time in improving legislative capability.,—'
Five major strategic components are included in the model to evaluate
the effectiveness of state legislatures:
* Functionality --including variables related to staff and facilities,
structural characteristics related to manageability, organization
and procedures, to expedite the flow of work and time allocation
and utilization, etc.
* Accountability—including factors affecting the coraprehensibility
in principle, public accessibility to the adequate information, and
internal accountability, etc.
38/ See Samuel Patterson, "The Political Cultures of the American
States," Journal of Politics, Volume 30, Number I (February 1968),
pp. 187-209.
39/ The Citizens Conference on State Legislature, State Legislatures:
An Evaluation of Their Effectiveness (New York: Prager Publishers,
1971), p. 3.
20
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* Information-handling capability—including activities of standing
committees, interim process, fiscal review and professional staffing,
etc.
* Independence--including requirements of independence of the legisla-
tive autonomy, of the executive branch and its operation, plus that of
interest groups, etc.
* Representativeness—including criteria of member and constitutents
identification, diversity, and effectiveness of the members, etc.
The study collected data and statistics reflecting on each of the com-
ponent variables by questionnaires mailed to legislators and legislative
staff members in all 50 states. The 50 states were then ranked
according to their indexes of effectiveness. Detailed recommendations
for each state based on its weakness and strength were finally
discussed and presented.
Francis developed some centralization indexes for state legislatures
based on responses from a 1963 sample of 838 state legislators rep-
resenting each house in all 50 states. Legislators were asked where
they thought the most significant decisions were made in their legis-
lature. Schlesinger employed tenure potential, appointive, budgetary
and veto powers to measure the governor's formal powers. Grumm selected
five variables in the model of legislative professionalism:
* Compensation of legislators (1964 to 1965);
* Total length of sessions during the 1963-64 biennium;
* Expenditures for legislative services and operations during the same
biennium;
* Number of bills introduced in the 1963-64 session; and
* A legal services score.
Lockard constructed a party integration index to evaluate the output of
the competitiveness and cohesion in state legislatures; Ranney, basing
his work on average percentage figures for popular vote won by Demo-
cratic gubernatorial candidates, for percent of seats held by Democrats
in state houses and senate, and for percent of all terms of governor,
21
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house, and senate in which Democrats control, developed some political
partisanship indexes.—'
All those studies cited above have been utilized as references and
basic data sources in the CCSL model. Each of them defined a specific
element in the political arena and then constructed a model to quantify
the outputs and performance or effectiveness of the legislative actions
or activities.
For criminal justice, the National Advisory Commission on Criminal
Justice Standards and Goals set up a system in vhich criminal justice
information systems were proposed. It recommends that each state
create an organizational structure to prepare a master plan for the
development of an integrated network of criminal justice information
systems and to provide identical and consistent data for analytical
purposes. The model includes systems for policy, courts and correc-
tions, among others. In cross-sectional models, the Advisory Commis-
sion on Intergovernmental Relations has, for many years, made regular
comparisons between revenues and expenditures among states and cities,
and the Urban Institute has also launched programs to measure the
effectiveness of government services.—'
For governments, two types of models are conventionally used to reach
public decision: normative versus positive. The normative approach
407 See Wayne Francis, Legislative Issues in the Fifty States (Chicago:
Rand McNally, 1967); Joseph Schlesinger, "The Politics of the
Executive," in Politics in the American States, H. Jacob and
K. Vines (eds.), (Boston: Little, Brown, and Company, 1965);
John Grumm, "Structural Determinants of Legislative Output,"
Legislatures in Developmental Perspective, A. Kronberg and L.
Musolf (eds.) (Durham, North Carolina: Duke University Press,
1970); Duane Lockard, "State Party Systems and Policy Output,"
in Political Research and Political Theory, Oliver Garceau (ed.),
(Cambridge: Harvard University Press, 1968); and Austin Ranney
"Parties in State Politics," op. cit., H. Jacob and K. Vines (eds.).
41/ For example, see National Advisory Commission on Criminal Justice
Standards and Goals, A National Strategy to Reduce Crime (Wash-
ington, D.C., January 1973); Advisory Commission on Intergovern-
mental Relation, City Financial Emergencies (Washington, D.C.:
U.S. Government Printing Office, 1973); and Urban Institute and
International City Management Association, Measuring the Effec-
tiveness of Basic Municipal Sciences (Washington, D.C.: The
Urban Institute, 1974).
22
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accepts well-defined objectives for governmental undertakings, and
selects specific policies and actions for achieving them. The positive
approach accepts the facts of reality and attempts to provide insight
into what will happen under given circumstances.
Dorfman and Jacoby constructed a positive benefit-cost model with
decision variables, costs, political and technology constraints to
achieve the goal of pareto optimality or to accomplish pareto admis-
siblity decisions—a condition under which there exists no feasible
alternative that some interested parties regard as superior and none
regard as inferior. This type of benefit-cost model is expected to
take into account social values of benefits and costs in addition to
private market values when political decisions are to be made posi-
tively. They have been widely adopted in public investment projects.—'
Rummel constructed a multidimensional model to analyze cross-national
and international patterns. With indicators representing various
patterns of national attributes and types of attributes—internal and
external, as well as behavior indicators between nations—Rummel
attempted to correlate international relations among the nations by
a wide-angle mathematical lens that filtered out all but the distinct
clusters of interrelated phenomena.—'
In short, most political models deal primarily with some special subject
within the political sciences, and are centered on issues of effective-
ness, efficiency, performance, and party evaluation. The overall
quality of life concerns must include the political elements, but the
latter by no means fully reflect the essential ingredients of the
former.
Sociological Models
The growing interest in social problems is evidently derived from
responses and reactions to the materialism that has traditionally
42/ Robert Dorfman and Henry Jacoby, "A Public Decision Model Applied
to a Local Pollution Problem," Economics of the Environment,
R. and N. Dorfman (eds.) (New York: W.W. Norton and Company,
1972); and Robert Dorfman, et al., Models for Water Quality
Management (Cambridge: Harvard University Press, 1972).
. J. Rummel, "Indicators of Cross National and International
Patterns," The American Political Science Review, Volume 63,
Number 1 (March 1969), pp. 127-147.
23
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pervaded the Western value system and ruled the capitalist society of
the United States. Marginal utility or satisfaction derived from a
higher level of consumption produced by great technological improvement
in the past decades has diminished substantially. Social issues such
as housing segregation, income distribution, discrimination and equal
rights, education, health and social justice and fairness, and welfare
are mounting concerns among the majority of Americans today. The
marginal disutility of these social problems rises in an accelerated
rate, surpassing the rate of marginal utility changes brought about by
material wealth growth.
Hamilton, Johnson, and Stafford, among others, utilized regression models
to measure wage or earnings differences between sexes. By isolating
factors (other than sex) to which wage differentials might be attrib-
uted, they found that discrimination against females exists and to a
significant degree the differences in earnings are attributed to sex.
In the same manner, regression models, varying in the specification
of functional relationships constructed by Becker, Bergmann, Marshall,
Welch, and others, also showed earnings differentials due to racial
discrimination.—
Rokeach and Parker developed a value survey model in which 18 terminal
values—desired end-states of existence (e.g., a comfortable life, a
sense of accomplishment, a world at peace and of beauty, social recog-
nition, self-respect, equality, security, freedom, happiness and mature
love, etc.) and 18 instrumental values—preferred modes of behavior
(e.g., ambitious, broadminded, capable, cheerful, clean, courageous,
forgiving, helpful, honest, independent, imaginative, logical, polite,
responsible, etc.) are employed for respondents to rank these values
in terms of "their importance as guiding principles in your life."
44/ See Mary Hamilton, "Sex and Income Inequality Among the Employed,"
The Annals of the American Academy of Political and Social
Science (September 1973), pp. 42-52; G. E. Johnson and F. P.
Stafford, "The Economics and Promotion of Women Faculty," Ameri-
can Economic Review, pp. 888-903; G. Becker, The Economics of
Discrimination (Chicago: University of Chicago Press, 1957), and
The Economics of Human Capital (New York, 1963); B. Bergmann,
"The Effects on White Incomes of Discrimination in Employment,"
Journal of Political Economy (August 1967), pp. 352-364;
H. Marshall, Jr., "Black/White Economic Participation in Large
U.S. Cities," The American Journal of Economics and Sociology,
Volume 31, Number 4 (October 1972), pp. 361-372; and F. Welch,
"Black/White Differences in Returns to Schooling," American Eco-
nomic Review (December 1973), pp. 893-907.
24
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The value survey has illustrated significant differences among people
related to many different kinds of attitudes, actions, and occupational
i 45/
roles.—
Most sociological models, even those whose theme does not focus on in-
dividuals, have to make assumptions about man. The assumptions may be
implicit—as in Parsons: expectations, need dispositions, cognitive
orientation and goal direction; or explicit and specific—as postulated
by Lenski, in terms of self-interest, creatures' habit, etc. The
"model of man" is said to be useful if it contains simple, testable
and refutable propositions in the following areas of sociological
concerns:
* The establishment of behavior;
* The maintenance of behavior;
* The extinction of behavior; and
* The modification of behavior (usually a combination of the first
and third).
Such a model can be used to describe large-scale processes and small
group phenomena. The behavioral models of man, best known in sociology,
are those by Romans, McGinnies, Simon, Skinner, and Kunkel and
Nagasawa.—'
45/ See M. Rokeach and S. Parker, "Values as Social Indicators of Pov-
erty and Race Relations in America," The Annals of the American
Academy of Political and Social Science, 388 (March 1970), pp. 97-
111, and The Nature of Human Values (New York: Free Press, 1973);
S. J. Ball and M. Rokeach, "Value and Violence: A Test of the
Subculture of Violence Thesis," American Sociological Review,
Volume 38, Number 6 (December 1973), pp. 736-749.
46/ See Talcott Parsons, The Social System (Glencoe: Free Press, 1951);
Gerhard Lenski, Power and Privilege: A Theory of Stratification
(New York: McGraw Hill, 1966); George Romans, Social Behavior:
Its Elementary Forms (New York: Harcourt, Brace, 1961), and
"Contemporary Theory in Sociology," Handbook of Modern Sociology,
R. E. Paris (ed.) (Chicago: Rand McNally, 1964), pp. 951-977;
Elliott McGinnies, Social Behavior: A Functional Analysis (Boston:
Houghton Mifflin, 1970); Herbert Simon, Models of Man (New York:
Wiley, 1957); B. F. Skinner, Beyond Freedom and Dignity (New York:
Knopf, 1971); and John Kunkel and Richard Nagasawa, "A Behavioral
Model of Man: Propositions and Implications," American Sociological
Review,Volume 38, Number 5 (October 1973), pp. 530-542.
25
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The application of multiple instruments for measuring structural charac
teristics of complex organizations was recommended by Pennings in order
to determine their convergent and discriminant validity with respect to
the degree of centralization and formalization, i.e., a combination of
the institutional approach which relies on documents and informants,
and the survey approach with questionnaires and interviews .
The causes and consequences of variations in community power structure
have been analyzed by Hawley. Reliable objective indicators of power
concentration are classified as the group of managers., officials and
proprietors in the labor force. The criticism has been made that the
development of social system models has been hampered by the lack of
the necessary methodology which takes into account the feedback effects.
To meet this objection, Liu, Anderson, and others proposed a simul-
taneous causal-effect equation model linking sociodemographic character-
istics of the population, socioeconomic, political, psychological, and
other variables to study the migration patterns and health service pro-
vision, respectively. The structural equations and reduced form
equations, of this type of models taken together, provide a means of
predicting the impact of governmental policies on migration and medical
care. —
To summarize, the sociological models, although covering a variety of
sociological elements ranging from individual behavior to institutional
organization, still are far from being able to take into account all
tangible and intangible factors affecting our quality of life. There
is an urgent need for a synthesized, fundamental framework in which
the quality of life factors, be they social, economic , political, or
environmental, can be systematically organized and structured in such
47/ Johannes Pennings, "Measures of Organizational Structure: A
Methodological Note," American Journal of Sociology. Volume 79,
Number 3 (November 1973), pp. 686-704.
Amos Hawley, "Community Power and Urban Renewal Success," American
Journal of Sociology (January 1963), pp. 422-431; Ben-chieh Liu,
"Impact of Local Government on Regional Growth," Proceedings of
American Statistical Association, Business and Economics Section
(1973) ; and James Anderson, "Causal Models and Social Indicators:
Toward the Development of Social Systems Models," American
Sociological Review, Volume 38, Number 3 (June 1973), pp. 285-301.
26
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a form that the interwoven relationships among those complicated quality
of life ingredients can be clearly described, presented, evaluated, and
analyzed. As a result of this need, several quality of life models have
been gradually developed in this country as well as in the rest of the
world.
QUALITY OF LIFE MODELS
In the preceding section, various models attempting to depict scientif-
ically the behaviors and interactions of the human being—the social,
economic, political, psychological, and environmental areas have been
briefly described in terms of the nature of model structures and varia-
tions in methodological development. One of the basic criticisms is
that the models, in general, focus on one of the quality of life elements,
but not all of them. The following review discusses in brief the quality
of life models in the U.S. and abroad.
Quality of Life Models in the U.S.
Conceptual models of the quality of life in the U.S., as pointed out
previously, offically started at least as early as 1933, when the report
on Recent Social Trends in the U.S. was issued. The report of the
President's Commission on National Goals, Goals for Americans, published
in 1960, significantly advanced the state of the art in modeling the
quality of life, and Social Indicators, 1973. produced by the Office of
Management and Budget, signifies the public interest in this kind of
research.
However, the combination of a theoretical model with empirical measure-
ments of the quality of life in this country at the state level was
first attempted by Mencken as early as 1931, but was not so well-known
until the work by Wilson, The Quality of Life in America, was published
in 1967.Ai/
49/ See John Berendt, "The Worst American State," Lifestyle Magazine
(New York: Lifestyle Magazine, Inc., November 1972), pp. 6-18.
and John Wilson, The Quality of Life in America (Kansas City:
Midwest Research Institute, 1967), and Quality of Life in the
U.S. - An Excursion into the New Frontier of Socioeconomic
Indicators (Kansas City: Midwest Research Insitute, 1970).
27
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Substantial efforts have been invested in the theoretical development
of quality of life models. For example, based on Maslow's classification
of needs, Mitchell, Logothetti, and Kanton defined the quality of life
levels and developed five quality of life scales. Garn, Flax, Springer
and Taylor, in the attempt to identify and classify the social indicators,
explored the indication relationship between consumption and produc-
tions to develop their interdependent models. Terleckyz constructed a
goal accounting system for performance measurement through the input-
output approach. The Ruggleses proposed the use of social and economic
accounts. Wingo expressed the quality of life by a microeconomic
definition, and Castle suggested that an integration of the quality of
life and economic affluence be reviewed and studied.—'
While Mencken selected variables in areas of wealth, welfare, health
and security, and crime affairs to measure the well-rounded picture of
the livable states, Wilson adopted as criteria the definition estab-
lished by President Eisenhower's Commission on National Goals to develop
the quality of life indexes, and assessed the life quality for each
state through nine components—status of individual, equality, demo-
cratic process, education, economic growth, technology change, agriculture,
living conditions, and health and welfare. Indexes for each of the
components were constructed either through the simple linear aggregation
method, or more sophisticated factor analyses, and the states were then
ranked accordingly.
States are not ideal territorial units for identifying regional varia-
tions in quality of life. Neverthless, the use of states can be
50/ See A. Mitchell, T. Logothetti, and R. Kanton, "An Approach to
Measuring Quality of Life," (Menlo Park, California: Stanford
Research Institue, 1971); H. Garn, M. Flax, M. Springer and
J. Taylor, "Social Indicator Models for Urban Policy - Five
Specific Applications,!1 (Washington, B.C.: The Urban Institute,
1973);N. E. Terleckyz, "A Goals Accounting System," paper pre-
sented in the annual meeting of the American Statistical Associ-
ation (St. Louis, 1974); R. Ruggles and H. Ruggles, "Social In-
dicator and a Framework for Social and Economic Accounts,"
paper presented at the Annual Meeting of the American Statistical
Association (St. Louis, 1974); L. Wingo, "The Quality of Life:
Toward a Microeconomic Definition," Urban Studies, Volume 10,
(1973), pp. 3-18; E. N. Castle, "Economics and the Quality of
Life," American Journal of Agricultural Economics (December
1972), pp. 723-735.
28
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justified on the grounds that many state programs have an important
bearing on social well-being, and at the present time data compiled by
states provide the only practicable way of examining the weakness and
strength of quality of life among states at a broad regional level.
Recently, Smith selected a wide range of different variables to repre-
sent as closely as possible the general definitions of social well-being
for the states. Seven components related to the variables are chosen
for empirical rating purposes: income, wealth and employment, the
environment, health, education, social disorganization, alienation and
participation, and recreation. Except for recreation, Smith collected
data and compiled the ratings of social well-being by components for
all the 50 states. In the meantime, Berendt also updated the study
of Mencken (Liu developed a similar model) and revised Wilson's study
with quality of life rankings computed for the 50 states and the
District of Columbia.A!/
The study by Liu differs from the others in that it started with a two-
dimensional mode, fundamental but not rigorous, reflecting the psycho-
logical and the physiological attributes of the quality of life, and
that it measured the quality of life for a particular point in time by
taking variable data from 1970, or years very close, in recognition of
the changes in the quality of life over time. In the model, data which
were not expected to be periodically published were not employed in
order to be consistent, so that future comparisons of the changes in
the quality of life among states can be made. In addition, Liu also
made an effort to describe and compare the empirical findings among
these studies and concluded that although income is a necessary con-
dition for the basic quality of life, the quality of life in the states
is not essentially associated with the level of income when the state
income is beyond that of the national level.—
In an endeavor to measure the quality of life changes in the state, the
Office of Planning and Programming in the State of Iowa has consistently
published An Economic and Social Report to the Governor for the past
several years. The quality of life components included in the report
range broadly from labor and personal income to lawful behavior
51/ See David Smith, The Geography of Social Well-Being in the U.S.
(New York: McGraw Hill, 1973); John Berendt, o£. cit., and Ben-
chieh Liu, Quality of Life in the U.S., 1970 (Kansas City:
Midwest Research Institute, 1973).
52/ See Ben-chieh Liu, "Variations in the Quality of Life in the United
States, 1970," Review of Social Economy, Volume 32, Number 2
(October 1974), pp. 131-147, and "Quality of Life: Concept,
Measure and Results," American Journal of Economics and Sociology,
Volume 34, Number 1 (January 1975).
29
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minority population. In the 1974 Annual Report of the Economic Policy
Council and Office of Economic Policy, the State of New Jersey, a chapter
was wholly devoted to the statistical profile of the quality of life
in New Jersey.—/ In tne report, issues on income, employment, health,
education, social well-being and security, and others were discussed.
In an attempt to describe and explain differences between cities in the
quality of life, Thorndike published two remarkable works, Your City
and 144 Smaller Cities, respectively, in 1939 and 1940. The quality of
life component studies for a special region, city or a group of the
regions or cities in this country have also proliferated. Among the
recent work, Bell and Stevenson constructed the economic health index
for Ontario counties and districts, Bullard and Stith presented urban
indicators and social disparity for community conditions in Charlotte,
Flaming and Ong, Jr.,prepared a social report for Milwaukee, and Lowry
analyzed the race and social economic well-being, in Mississippi, while
Flax made comparisons over urban indicators for 18 large metropolitan
areas; Lineberry, Mandel and Shoemaker defined and measured Community
Activity Indicators for Little Rock, Arkansas; Monroe, Louisiana;
Shawnee and McAlester, Oklahoma; and San Marcos and Midland, Texas;
and Coughlin measured the attainment along goal dimensions in 101
metropolitan areas.—'
53/ See Office for Planning and Programming, Iowa, The Quality of Life
In Iowa: An Economic and Social Report to the Governor for 1973
(Des Moines, Iowa, 1973); Department of Treasury of New Jersey,
Seventh Annual Report (Trenton, New Jersey, 1974).
54/ See E. L. Thorndike. Your City (New York: Harcourt, Brace, and Com-
pany, 1939), and 144 Smaller Cities (New York: Harcourt, Brace,
and Company, 1940); W. H. Bell and D. W. Stevenson, "An Index of
Economic Health for Ontario Counties and Districts," Ontario
Economic Review, 2 (1964), pp. 1-7; J. L. Bullard and R. Stith,
Community Conditions in Charlotte, 1970 (Charlotte, North Carolina:
The Charolotte-Mecklenburg Community Relations Committee, 1974);
K. H. Flaming and J. N. Ong, Jr. , A Social Report for Milwaukee:
Trends and Indicators (Milwaukee, Wisconsin: Milwaukee Urban Ob-
servatory, 1973); M. Lowry, "Race and Socioeconomic Well-Being:
A Geographical Analysis of the Mississippi Case," Geographical
Review, 60 (1970), pp. 511-528; M. Flax, A Study in Comparative
Urban Indicators: Conditions on 18 Large Metropolitan Areas
(Washington, D.C.: The Urban Institute, 1972); R. Lineberry,
A. Mandel and P. Shoemaker, Community Indicators: Improving
Communities Management (Austin, Texas: Lyndon B. Johnson School
of Public Affairs, The University of Texas, 1974); R. Coughlin,
"Attainment Along Goal Dimensions in 101 Metropolitan Areas,"
Journal of the American Institute of Planners, Volume 39, Number
6 (November .1973), pp. 413-425.
30
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Resources dedicated to quantification of the quality of life among urban
areas have tended to be increasing at an accelerated rate not only be-
cause people are more and more concerned about their life quality and
the associated causes and effects, but also because the task of
measuring the quality of life in itself is challenging and interesting.
For example, Torres tried to measure the quality of life in America's
major metropolitan areas by a very narrow definition, and Marlin attempted
to rank the performance of 31 cities by a few economic variables. After
Elgin found that the quality of life in the country goes down as city
size increases, Louis launched a project to see which are the worst
cities among the largest 50.— Currently, the Kettering Foundation
sponsors research in identifying the factors for urban success, the
Council on Municipal Performance is conducting evaluations among cities
in their respective performance on various quality of life components,
and Stanford Research Institute is engaged in modeling the minimum
acceptable level or standard of quality of life from the viewpoints of
social, economic, political, and environmental criteria, in conjunction
with the model and results presented in this study.—
Quality of Life Models in the Rest of the World
There is now immense interest throughout the world in better social
measurement, in assessing the fruits of economic growth, and in measuring
needs and the distribution of benefits. Everywhere social statistics
and the measures of quality of life have increased priority.
551 See Juan Torres, "The Quality of Life in America's Major Metropol-
itan Areas," The Conference Board Record, Volume 11, Number 2,
(1974), pp. 51-64; John Marlin, "Jobs and Weil-Being:
Which Cities Perform the Best," Business and Society Review
(Summer 1974), pp. 43-54; Duane Elgin, City Size and the Quality
of Life (Menlo Park, California: Stanford Research Institute,
1974); Arthur Louis, "The Worst American City," Harper's Magazine
(January 1975), pp. 67-71.
56/ Geoff Ball is working on the research study sponsored by the Ket-
tering Foundation, and 0. W. Markley and Maryland Bagley are
working on the Stanford Research Institute's Project, funded
by the Environmental Protection Agency; for the Council on Muni-
cipal Performance projects, see for example, The Wealth of Cities,
Municipal Performance Reports, 1.3 (April 1974).
31
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The Statistical Office of the United(Nations has launched a significant
project, "Towards a System of Social and Demographic Statistics," (SSDS)
and a technical report was prepared by Stone in 1973.~' In principle,
the system should cover all areas of social life which are of interest
or concern, and for which it is thought necessary to have a policy and
to attempt remedial action. The aim of this project is to give a
systematic account of the statistical information needed for the
following subjects:
* The size and growth of the world's population
* Population density and urbanization
* High-level consumption and its growth
* National resources and the environment
* Learning activities
* Earning activities
* Family grouping
* Housing conditions and neighborhoods
* Leisure
* Social mobility
* The distribution of income, consumption and accommodation
* Social security and welfare service
* Health and medical care
* Public order and safety
. \
57/ See Richard Stone, Towards A System of Social and Demographic
Statistics (New York: United Nations, ST/STAT. 68, July 1973).
32
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SSDS represents one of the most comprehensive models formalizing current
needs and developments in social indicators related to the world's quality
of life. It began with a simple set of input-output matrices concerned
basically with population, education and manpower, but has grown into
other areas of leisure, health, housing, security, and social mobility.
The Organization for Economic Cooperation and Development (OECD), which
comprises the more advanced industrial nations, has also recently approved
the work designed to develop a set of social indicators which can
jointly measure the social indicators of well-being in the member
countries. The first stage of the work has consisted of identifying
and agreeing upon what are the most important and conceivably measur-
able components of the quality of life from the viewpoint of present
and potential government interest. The next step will be, logically,
to find or to design the necessary method of measurement.— A total
of 24 fundamental social concerns common to most OECD countries are
listed in the model. They are described in the following categories:
* Health
* Individual development through learning
* Employment and quality of working life
* Time and leisure
* Command over goods and services
* Physical environment
* Personal safety and the administration of justice
* Social opportunity and participation
The overall project objectives under the OECD's social indicator pro-
gram are to identify the social demands, aspirations, and problems which
are or will become likely major concerns of social economic planning
processes, to measure and report changes relative to these concerns,
58/ See David E. Christian, Social Indicators, the OECD Experience
(Paris: OECD, June 1974).
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and to better focus and enlighten public discussion and public decision
making. In conjunction with the efforts of OECD, a number of models
have been developed for the member countries. Work for Germany can
be found, for example, in Gehrmann and Koelle; and studies for Sweden,
Finland, Japan and the United Kingdom have been completed in varying
form by Elmhorn, Allardt and the Economic Planning Center, Hanayama
and the Economic Planning Agency, and in Social Trends, respectively
Furthermore, Maruo has also briefly compared the welfare of Japanese
people to that of the people in the U.S., Sweden, Germany, England, Italy,
and France. Within his welfare category, he studied levels of needs—
basic (income, safety and health), amenity (natural, living and working
environment), and higher needs (educational, leisure, and community
participation). While Michalos employed aggregate indicators at the
59/ See Freidhelm Gehrmann, "Vorschlage zu Forschungsstrategien in
Rahmen der Quantifizierung der stadtischen Lebensqualit'dt,"
(Paris: OECD Sector Group on the Urban Environment, Volume 25-26,
July 1974); Uberblick liber den Stand der Forschung auf den Gebiet:
Quantifizierungsversuche der (st'ddtischen) Lebensqualit'a't (Mono-
graph, Universitat Augsburg, Augsburg, July 1974); and "The
Definition of Fundamental Indicators for Employment and Services"
paper presented at the second meeting of the OECD Working Group
on Environmental Indicators (Paris: October 3-4, 1974); and
H. H. Koelle, "Entwurf eines zielorientierten, gesamtgesellschaft-
lichen Simulations Models zur Unterstlitzung der Ziel-, Aufgaben-
und Finanzplanung," (Monograph, Zentrum Berlin fur Zukunftsforschung
e.v., 1974); Kerstin Elmhorm, "Life Quality and Environmental
Investigation" (Monograph, the Swedish National Board of Health
and Social Welfare, July 1974); Economic Planning Center, "Quality
of Life, Social Goals and Measurement" (Monograph, Division of
the Economic Council of Finland, 1973); Erik Allardt, "About
Dimensions of Welfare: An Exploratory Analysis of A Comparative
Scandinavian Survey" (Monograph, University of Helsinki, 1973);
Yuzuru Hanayama, "Development and Environment in Japan," Inter-
nationales Asienforum, Volume 4 (1973), pp. 406-415; and Japanese
Economic Planning Agency, White Paper on National Life; The Life
and Its Quality in Japan (Minister of State, Japan, 1973); and
Government Statistics Service, Social Trends, Number 4 (December
1974, London).
34
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national level to compare the quality of life between U.S. and Canada,
Macy and Foster used disaggregated city indicators to evaluate that in
U.S. and Canadian cities.60/
Almost all these models employed the objective social indicators or
the physical approach with which secondary data on statistics were col-
lected, organized, computed, and analyzed. Opinion surveys on the
psychological approach, seeking for firsthand information to quantify
subjectively quality of life, have just recently started. Among them,
the University of Michigan's survey project in measuring the quality of
employment and the job satisfaction among workers is a well-known
one. In addition, pollsters from Gallup International Institute in
Canada, Africa, and points between, are asking people all over the
world a series of questions about happiness, personal satisfaction and
hopes and concerns for the future.— While the Center for Social
Indicators, Social Science Research Council, has periodically reported
through its Social Indicators Newsletter the quality of life projects
in the U.S., the Social Indicators Research, an international and
interdisciplinary journal for quality of life measurement, edited by
Alex Michalos in Canada, has begun publication for all theoretical
and empirical work related to the conceptual development and technical
measurement of the quality of life throughout the world.
_60/ See Naoni Maruo, "Measuring Welfare of the Japanese People—including
International Comparison," Internationales Asienforum, Volume 4
(1973), pp. 550-554; Alex Michalos, "Methods of Developing Social
Indicators," and Bruce Macy and Robert Foster, "A Tentative Com-
parison of Metropolitan Quality of Life, Canada and the U.S,"
papers- presented at the Conference on Growth Centers and Develop-
ment Policy, Halifax, Nova Scotia, Canada, April 9-10, 1975.
61/ See Stanley Seashore, "Job Satisfaction as an Indicator of the
Quality of Employment," Social Indicator Research, Volume 1,
Number 2 (September 1974), pp. 135-169; and Robert Quinn and
Linda Shepard, The 1972-73 Quality of Employment Survey (Ann
Arbor, Michigan.University Institute for Social Research, 1974);
and New Ways. quarterly report by the C. F. Kettering Foundation,
Fall, 1974.
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CHAPTER III
ECONOMICS IN CONTEMPORARY SOCIETY
WELFARE ECONOMICS AND THE QUALITY OF LIFE
Economics has long been defined as a scientific study that deals with
the allocation of scarce resources among alternative uses to satisfy
unlimited human wants. It is fashionable for the modern positive
economist to follow Robbins1 argument that ethical value judgments have
no place in scientific analysis, because ethical conclusions cannot be
evaluated in the same way that scientific hypotheses are tested and
verified.i' However, it is invalid on the basis of this observation
to preclude economists from studying "welfare economics" or examining
the consequences of various value judgments. Just as the study of
comparative ethics is itself a science, so in welfare economics a great
many analyses do not require interpersonal comparisons of utility.
Besides, the welfare function need only be ordinally defined or techni-
cally transferable among the relationships of preferences: e.g., better,
worse, or indifferent.— Furthermore, the complexity of our post-
industrial society requires that economists step out from the orthodox
framework of pure competition, guaranteed full employment, efficient
production, and accelerated growth. Externality, social costs, depleted
natural resources, polluted environments, accelerated inflation, and a
number of other social problems which adversely affect our quality of
life, are waiting for solutions".
Much of the traditional academic teaching and research in economics has
been criticized for its lack of empirical relevance, immediate
I/ For instance, see L. Robbins, An Essay on the Nature and Significance
of Economics Science (London, 1932).
2j For an equal argument, see Paul Samuelson, Foundations of Economic
Analysis (New York: Harvard University Press, 1965), Chapter 8.
36
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practical impact, and adequate scientific means to meet the practical
problems.^/ The decisive weaknesses in neoclassical and neo-Keynesian
economics lie in the assumptions which tend to destroy its relation with
the real world, especially in eliding "power" by making economics a non-
political subject. Thus, according to Galbraith, the neoclassical and neo-
Keynesian economics are relegating their players to the social sidelines
where they either call no plays or urge the wrong ones when the prob-
lems of our world are increasing, both in number and in the depth of
their social affliction.^/ Kenneth Arrow has also admitted that in-
equality of economic development among groups and regions within a
country, provides complicated difficulties for neoclassical theory.—'
Furthermore, there are new campaigns against the reigning fashion of
the traditional political economy as we search for material growth and
wealth. Many economists are beginning to tackle the issues of human
values. Growth, it is charged, distorts national priorities, worsens
the distribution of income, and irreparably damages the social and
natural environments in which we all live.
The conventionally used national health indicator, the Gross National
Product (GNP)--by which the growth in national production of goods and
services per unit of time per capita has been measured, and national
strength has been evaluated for many decades--has also been under
fire recently. Nordhaus and Tobin characterize the GNP measure as an
index of production, not of consumption, and much less of economic
welfare.—' The national income accounts largely ignore the many sources
of utility or disutility that are not associated with market operation
and measured by market values. For example, Nordhaus and Tobin indicate
that defense costs are intermediate rather than final demand, while
educational services and leisure and environmental amenities are direct
rather than indirect sources of consumer satisfaction. They started with
inadequacies of the conventional measure of national wealth—Gross
National Product (GNP)--and developed some theoretical adjustments
needed to convert GNP into a measure of Net Economic Welfare (NEW).
3_/ See Wassily Leontief, "Theoretical Assumptions and Nonobserved
Facts," American Economic Review (March 1971), pp. 1-7.
4_/ John K. Galbraith, "Power and the Useful Economist," American Economic
Review. (March 1973), pp. 1-11.
5/ Kenneth Arrow, "Limited Knowledge and Economic Analysis," American
Economic Review (March 1974), pp. 1-10.
6/ For example, see William Nordhaus and James Tobin, "Is Growth
Obsolete," Economic Growth, 50th Anniversary Colloquium V
(New York).
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Empirically, they estimated that the NEW grew at only two-thirds the
annual rate of per capita GNP over the period of 1929 to 1965. From
purely technical viewpoints, Cole and Ruggles also have criticized
the errors in the measurement of GNP.—
"Quality of Life" (QOL) is a new name for an old notion. It denotes
a set of wants, the satisfaction of which makes people happy. It re-
flects a combination of the subjective feelings and objective status
of the "well-being" of people and the environment in which they live
at a particular point in time. Dissatisfaction either with GNP as an
accurate measure of social welfare or with the growth of GNP as a
goal for national life, has led to a demand for some social indicators
which can be used to set policy priorities, and to measure the extent
to which we are satisfied with our human and environmental conditions.
In addition to the concern about efficient production with limited
resources to meet those unlimited human wants, new welfare economics
stresses even more an equitable system of distribution among groups
and regions as well. A robust GNP provides basic needs for an undefined
yet ever increasing level of subsistence, but a healthy economy enables
more people to pursue their aspirations and happiness beyond the level
of physical satisfaction, whether acquisitive or contemplative.
The quality of life indicators or social indicators represented by a
host of statistics on socioeconomic, political and environmental condi-
tions may offer clues to human attitudes and behavior, and societal
performance over time. The statistical compilation of those social ab-
stractions, if their limitations are properly understood, would certainly
be useful to the extent they provide meaningful measurement of the
actual results of public and private programs designated to improve our
quality of life. The social turmoil of our age is reflected in every-
thing from rising crime and inflation rates to the search for energy
resources and for psychic tranquility through exotic religions.
Yet happiness and inner harmony have never been directly, independently
achievable ends, but rather the by-products of philosophies, goals, and
values which are simultaneously determined by others in the society.
Social indicators, when properly constructed, interpreted, and used,
can shed light on many welfare issues involving value judgments and
ordinal utility comparisons among individuals. These, in turn, may
enable intelligent decision makers to devise timely, efficient policies
leading to a betterment of the quality of life for many individuals in
the community, without worsening it for others in the same community.
2J See R. Cole, Errors in Provisional Estimates of Gross National
Product (New York: National Bureau of Economic Research, 1969);
and N. Ruggles and R. Ruggles, The Design of Economic Accounts
(Ibid, 1970).
JO
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Man does not live by bread alone, and economists are not all merely
concerned with the income or GNP statistics. As Alfred Marshall stated,
the economist, like everyone else, must concern himself with the ulti-
mate aims of man. The issues of poverty within affluence, of discrimi-
nation within equality, of environment preservation within economic
performance, etc., are controversial, and involve value judgment.
Economic analysis can contribute a great deal to the elucidation of
these issues. What do economists economize? It is "love," said Sir
Dennis Robertson, for that is the scarcest.commodity in the universe.
Then what do economists attempt to optimize? The answer is, the
quality of life or happiness, for that has been expressed often as a
ratio of material to desire. As a society becomes more comfortably
situated, the more it can afford to indulge its distaste for a purely
pecuniary motivation based on self-desire.- However, as quality of
life is a function of both material wealth and psychological desire as
illustrated in the subsequent section, the two input factors are
normally interrelated. Thus, the objective is to maximize the ratio,
rather than the numerator alone.
A PRODUCTION APPROACH TO QUALITY OF LIFE
As the nation is rapidly approaching its 200th anniversary, the majority
of Americans become more and more disturbed and feel less and less con-
tent with the quality of life in the U.S.— In spite of our rapid growth
in per capita income and the highest level of living standard among all
nations in the world, dissatisfaction among our citizens grows at an
increasing rate with our social, political, and environmental problems
such as urban crimes and ghetto slums, political scandals, the genera-
tion of waste and pollution, inflation and the energy crises, etc. The
integration of the quality of life concept into the general framework
of production theory in the conventional microeconomic analyses becomes
an important and as yet unexplored subject.
JJ/ For related material, see Paul Samuelson, Economics (New York:
McGraw-Hill, 8th Edition, 1970), Chapter 39, and Emery Castle,
"Economics and the Quality of Life," American Journal of Agricul-
tural Economics (December 1972), pp. 723-735.
9/ For instance, see "What America Thinks of Itself," Newsweek
(December 10, 1973), pp. 40-48.
39
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An attempt to accomplish this task will be outlined in this chapter.
To begin with, we feel that the structure of our systems not only in-
fluence the degree to which the members in the system can maximize
their quality of life at any given point in time, but also shape the
value concept as to what life is all about and how, in general,
an individual's achievement can be revealed and ranked when compared
with those of others. Therefore, the state of the quality of life
for any individual is interdependent via the following three mechanisms:
the intrapersonal capability of the individual, the interpersonal aspects
with other individuals, and the political system or society in which
they all live as members. Any exogeneous changes in one of these
components will result in changes in others and, as a result, there
will be feedback effects, too. In other words, the so-called "arena
of happiness" consists of three basic components, namely, the self,
the other, and the societal system.—'
Man is a "wanting" creature. The nature of human activity consists of
his persistent effort and of his failure to reach a state of complete
satisfaction. No sooner is one want satisfied than another surfaces
to take its place. As Maslow clearly stated:
The appearance of the drive or desire, the action that it
arouses, and the satisfaction that comes from attaining
the goal object, all taken together, give us only an arti-
ficial, isolated, single instance taken out of the total
complex of the motivational unit. This appearance practi-
cally always depends on the state of satisfaction or dis-
satisfaction of all other motivations that the total
organism may have, i.e., on the fact that such other pre-
potent desires have attained states of relative satis-
faction. Wanting anything in itself implies already
existing satisfactions of other wants.—'
The essence of self is animation and ambition. The movements within
the happiness-seeking arena are incessant. There is no static ground
on which a motionless, tranquil arena will be sustained as long as the
10/ For some empirical work on the universally sought happiness in the
arena, see Edward Scott and M. Erick Wright, An Arena of
Happiness (Springfield, Illinois: Charles C. Thomas, Publisher,
1971).
11/ Abraham Maslow, Motivation and Personality (New York: Harper and
Row, Second Edition, 1970), p. 24.
40
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"self" exists and activates. Consequently, the degrees of the quality
of life which an. individual produces and enjoys, vary not only among
persons and places, but also in time.
In order to optimize an individual's life quality, which encompasses
matters of discovering one's true self, i.e., his "self" development
of latent potential and self-actualization, it is necessary, according
to Maslow, that needs on two levels be met—basic needs and growth
needs. The basic needs include the physiological needs, the safety
and security needs, the belongingness and love needs, and the esteem
needs. The growth needs consist of those which psychologically develop
and actualize one's fullest potentialities and capacities in relation
to others in the community. Thus, what constitutes one's quality of
life, in both a biological and psychological sense, must be related to
the extent of meaningfulness of, and satisfaction produced by, one's
existence in an organized human society. Each member of our society
owns certain amounts and varieties of private goods, and shares the
use of some public goods and services, such as schooling, housing,
medical care, police and fire protection. Concomitant with these
basic and primary desires and needs, an individual develops secondary
needs, among which the important ones are love, esteem, dignity, belong-
ingness, lack of fear and anxiety, and an equal opportunity for self-
actualization and for enjoying the prosperity, accomplishment and
happiness of the entire society.
In defining the quality of life, Professor Wingo aptly states:
While the quality of life is clearly a Good in the ethical
sense, not everyone would agree immediately that it is a
good in the economic sense yet, that people aspire to it,
means that it is scarce and that people are willing to
surrender other kinds of satisfaction for it. In this
sense the quality of life is an economic good. Even if the
quality of life were confined to such nonreproducible
elements of nature as an appealing landscape, it must be
somehow rationed, and the land market affords such a
rationing process. If such benefits cannot be captured,
contained, and withheld from others, so that many may
enjoy it without paying for it, as is the case with
common property resources, it enters into the production
and consumption decisions of firms and individuals. If
the quality of life consists mainly of reproducible goods
-------
whose consumption cannot be restricted to particular consumers,
then it fits the definition of a public good to which com-
munity resources will be allocated. If the quality of life
fits any of these alternative formal characteristics, we
17 /
have reason to think of it in economic terms.—'
In addition, the very name of economics suggests economizing or maxi-
mizing and Marshall's Principles of Economics dealt much with maxima
and minima with which most economists have been occupied..!!/
Thus, the quality of life (QOL) that each individual (i) attempts to
maximize may be expressed as an output function with two factor inputs
as arguments--the physical (PH) and the psychological. (PS)--a portion
of which he owns and a portion of which he shares with other people in
the community at any given point of time (t):
= F (PHit, PS ) CD
It should be noted in passing that the input factors are not completely
independent. In addition, they can be employed in varying proportion
in the production of QOL. The physical inputs consist of the bundles
of material goods and services which satisfy most of basic needs of
human beings, while the psychological inputs are mostly self-actualized
and developed. It is possible that the former inputs can be used as
substitutes to a certain extent for the latter inputs, such as lack of
fear, anxiety feelings of being loved and respected, and awareness of
beauty. Although deprivations of one's ownership of physical goods and
services below the subsistence level are most serious and physiological
survival and/or psychological health is a hazard, depreciations in
psychological inputs could also impoverish considerably the affluent
society. That both PH and PS play an important role in determining the
quality of life is vividly manifested by the growing discontent of
today's Americans.
12/ Lowdon Wingo, "The Quality of Life: Toward a Microeconomic Defi-
nition," Urban Studies. Volume 10 (1973), p. 5.
13/ See Paul A. Samuelson, "Maximum Principles in Analytical Economics,"
Science, Volume 173 (September 10, 1972), pp. 991-997.
42
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In a recent survey conducted by Newsweek, 45 percent of the respondents
believe that the quality of their lives has been growing worse since
1963, and only 35 percent felt it has improved .M' An explanation for
this paradox lies in the fact that wealth is only a necessary, but not
a sufficient condition, for the production of a normal level of quality
of life. In terms of graphical illustrations, for a stipulated level
of QOL, only a portion of the "normal" iso-quality curve is relevant for
our analysis; that is the segment which is downward sloping and convex
to the origin as shown in Diagram 1, say, aa1. An iso-quality curve
is the locus of points which are representations of combinations of
factor inputs (PH) and (PS) such that the level of QOL produced is the
same for all combinations of the two input factors. Along this iso-
quality curve, varying proportions of physical and psychological inputs
can be employed to yield the same level of satisfaction derived from
the realized quality of life, and a person would feel equally happy
(or unhappy). Analogous to an iso-quant curve in production theory,
the availability of additional input from one category while holding
the amount of the other input constant, beyond a certain level, will
not enable an individual to acquire a better quality of life. For
instance, an input of oy1 of (PS), and ox1 of (PH) will produce the
same level of QOL, i.e., Q^, as does the combination of oy and ox or
oy-j and ox., of (PS) and (PH), respectively. However, additional input
of PH in excess of ox1 units, given (PS) input of oy1, will not. produce
a greater level of QOL than Q^; neither will any additional PS in excess
of oy with given ox of PH contribute to enhance the happiness of an in-
dividual when compared with the situation that he is at a1.-^' There is
Diagram 1
PS/time
XI
y1
0
x xj x' PH/time
14/ See "What America Thinks of Itself," Newsweek (December 10, 1973),
p. 45.
15/ However, it is conceivable in reality that an individual may feel
less and less happy with a substantial increase in PH input which
induces some loss in PS input. Typical examples are the broken
marital relationships and suicide cases among the wealthy persons.
For instance, see R.A. Easterlin, "Does Money Buy Happiness,"
The Public Interest, 30 (Winter, 1973).
43
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a saturation level with both the inputs beyond a or a1. Consider a
higher level of satisfaction as represented by iso-quality curve Q£
which lies uniformly above Qp Improvements in QOL can be achieved
or produced by greater amounts of both inputs PS and PH, or by a
greater amount of either input, with unchanged remaining input, or
even by elimination of one input, but a sufficiently large increase
in other input.
The segment aa1 on iso-quality curve Q. is assumed to be twice differ-
entiable, which implies that the curve is smooth. PH and PS are gen-
erally not grossly perfect substitutes. Convexity is assumed in the
sense that the marginal rate of technical substitution between these
two inputs is diminishing. The convexity property of the iso-quality
curve implies that d2 (PS)/d (PH)2 > 0. The rate of technical substitution
between (PH) and (PS) can be obtained by total differentiation of
the QOL production function.
For a given iso-quality curve, d(QOL) = 0, and thus (noticing that
both marginal contributions are assumed to be nonnegative):
d(PS) = -6(QOL)/ 5(QOL) < 0
d(PH) 6(PH) / 6 (PS)
The iso-quality curves are shown to be downward sloping and to the right.
Further, these negatively sloped iso-quality curves are convex to the
origin, as shown in Diagram 1, if d (PS) _ d[d(PS)/d(PH)3 > 0 or
d(PH)2 "
44
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d2 (PS) j d |-6(QOL)/6(PH) 1 _ d<-zh/zs) =
d (PH)2 d(PH) L5(QOL)/6(PS) J
-1
V
Zss (Zh> - 2 Zsh + Zhh
Where Z, = 6(QOL)/6(PH)
n
Z - 6(QOL)/6(PS)
> 0
s
Normally, we expect Zs^ to be nonnegative; therefore, Zgs and
must be negative, or the rate of change of the marginal contributions
of both factor inputs must be diminishing in order to assure the
convexity property of the iso-quality curve. Since the rate of tech-
nical substitution (RTS) is defined as the negative of 6(PS)/6(PH),
convexity also implies a decreasing RTS between these two factors,
i"e">
-6 (PS)/6 (PH) < 0.
It is assumed that the QOL production function is homogeneous. However,
the degree of homogeneity may be greater or less than one: i.e., the
returns to scale may be increasing or diminishing. The case of increasing
returns to scale is shown in Diagram 1 by the movement from Q, to Q^.
Note that Q2 represents twice and Q3 three times the intensity of
satisfaction of Q, and the spacing between Qj_, Q2> and Q3 shrinks
more than proportionately. The movement from Q3 to Q,, on the other
hand, reveals the decreasing returns to scale portions of the QOL
production function, i.e., to maintain an equal increase in happiness,
more than proportional amounts of PS and PH are required. In addition,
the iso-quality curves are assumed to be nonintersecting in the relevant
range.
A rational individual attempts to maximize his overall QOL production,
subject to certain capability constraints. Perceive a situation of no
constraints of any form, or of limitless capability of a human being;
each individual would move to the bliss point at which all his desires
are fully satisfied. Unfortunately, that is not the case in reality.
Each one has only 24 hours a day to spend in securing his PH and PS
inputs for production of his QOL. Observe an individual's capability
45
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to exchange PH and PS inputs is limited by the social, economic,
political conditions, and environments in which he lives. In addition,
the ability to acquire and to share with others the total PH goods
and services available in a society depends strategically upon the
individual's own economic wealth. On the other hand, there are restric-
tions on each individual's effort to secure PS inputs. For example,
the amount of PS acquired is determined in part by one's degree of
willingness to exchange resources and efforts for spiritual and
psychological inputs, such as esteem, belovedness, belongingness,
feeling of security, individual dignity and integrity, etc., that
other people in the society are willing to render to him. As expected,
the esteem, security and dignity also depend, to some extent, on PH.
Diagram 2 shows various forms of the capability constraints or iso-
capability curves that an individual may possess at any particular
point of time in his life span.
Diagram 2 PS/ti
i me
PH/time
The points on the iso-capability curves indicate the maximum possible
combinations of PS and PH that an individual is able to secure.
Consider the case of the end points of the iso-capability curve, say
for A. Point y(x) indicates the maximum quantity of PS(PH) obtainable
if the amount of PH(PS) is zero. Similarly, for individual C, the
maximum psychological intake, by foregoing all physical, goods and
services, he is able to secure is oy1. The iso-capability curves for
both A and C are concave to the origin, implying that the rate of
capability transformation between (PS) and (PH) for these two persons
are diminishing—more than proportionate PS must be sacrificed in order
to secure additional PH inputs. Consider the case of perfect sub-
stitutes between PH and PS, for individual B. The iso-capability curve
for B is a straight line, indicating that PH(PS) can be substituted
for PS(PH) at a fixed ratio. Although B's capability constraint lies
between those of A and C, the three persons are capable of acquiring
one common combination of PH and PS that is the intersection of the
three iso-capability curves, as shown at N. A special iso-capability
curve for some special individual may even look like YMX', or Y^NX',
i.e., a kinked one.
46
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We stated earlier that rational individuals are usually maximizing
their quality of life production subject to their capability constraints,
Given the iso-quality map and the iso-capability curve (xy) of an
individual for any given point in time as shown in Diagram 3, the maxi-
mum level of QOL of that individual is attained when the iso-quality
curve is tangent to the iso-capability curve. To be specific, this
individual is most satisfied in his life at the level of Q^ by
combining Oa units of physical goods and services and 0, units of
psychological inputs, given the limit of his capability by that time
is xy. Note that he is neither capable of producing Q^, due to his
own capability constraint, nor would it be efficient by organizing
a combination of PS and PH other than at N, say, at M, in the sense
that he would end up with a lower iso-quality curve, Q^. Thus, the
equilibrium position will be at the point where the slope of the iso-
quality and that of the iso-capability curve are identical.
Diagram 3 PS/time
M^_
0 ax X PH/time
Undoubtedly, condition and environment in which an individual lives
changes from time to time. It is not unreasonable to assume that an
individual's ability and capability improve as one grows in age.
During a lifetime, although it has been observed that an individual's
iso-capability curve can switch, say from xy to x'y* or vice versa as
shown in Diagram 2, the iso-capability curve for a "normal" individual,
in general, is expected to shift onward in the east-north direction,
i.e., from xy to XY, as shown in Diagram 3. In the former case, the
individual's QOL may be improved, unchanged or worsened depending upon
the way that the iso-capability curve is being shifted. The individual
in the latter case, can be shown to be always better off than before.
Consequently, a "normal" person, experiencing outright shift of the
iso-capability curve over time would have a QOL expansion path, say
N'NP'P. The QOL expansion path is derived by connecting equilibrium
points N1, N, P", P at each point of time.
47
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The QOL expansion path generally exhibits a "staggering effect." That
is, the path starting from the origin, initially may lean more toward
the horizontal axis (PH) because of the greater importance in satisfying
the basic needs or the Darwinian struggle for one's physical survival.
Beyond a certain level of the basic needs being satisfied, the expan-
sion path will lean more towards the vertical axis (PS). The basic
needs are, in essence, the biological and physiological needs. As
Maslow stated, "Frustration of basic needs creates psychopathologi-
cal symptoms, and their satisfaction leads to healthy personalities. "JL!i'
A person who is lacking food, safety, love and esteem would probably
hunger for food more strongly than for anything else.
The QOL expansion path may exhibit a point of inflection, say at N1 at
which some basic needs for survival are met and the individual begins
to aspire for more inputs from the psychological arena relative to the
physical domain to enrich his QOL production, say from Q, to Q2 and to
Q3« This is the plausible situation because the marginal productivity
of PH is diminishing, as PS increases less proportionately than does
the PH input. A greater increase input from PS relative to that of
PH beyond N' will move the individual into the increasing returns to
scale portion of the QOL production function. Analogously, the greater
increase in input of PS, relative to PH, will result in relatively
high productivity of the latter in the QOL production, when the level
of Q3 is achieved. An inflection point on the expansion path is found
at N. Along the same line of argument inflection points, such as P1
and P, can be logically located. In short, a QOL production expansion
path of a "normal" individual, with regard to his span of life time,
may simply take the staggering form of 0 N' N P1 P.
The range of the QOL expansion path is shown between OR and ORQ.
OR and OR^ are obtained by connecting the linked points on the iso-
quality curves, or the points beyond which the curves become vertical
and horizontal, respectively. The QOL expansion path, for a spiritualism-
oriented person, will bend toward the OR limit, whereas for a materialism-
s
oriented person, the expansion path will be biased toward the ORn
limit over his life horizon.
16/ Abraham Maslow, Toward A Psychology of Being (Second Edition,
New York: Van Nostrand Reinhold, 1962), pp. 50-51; Motivation
and Personality (Second Edition, New York: Harper and Row,
1970), pp. 36-37.
48
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Consider a special case in which the second derivatives fail to exist
for either the capability curve or the iso-quality curve, as shown in
Diagram 4. In this case, PH and PS are perfect substitutes for each
other in the production of QOL for a particular individual within the
range of Rg and R^. The marginal rate of substitution between the two
inputs is constant. Another special case involves the use of PH and
PS, in fixed proportion, in producing any level of QOL, as shown in
Diagram 5; the expansion path is, therefore, represented by a line
radiating from the origin and passing through all the corner points
of the QOL iso-quality curves. Additional inputs beyond the corner
points, while holding the other input constant, will not produce a
higher level of QOL for this individual.
Diagram 4
PS/time
Diagram 5
PS/time
PH/time
PH/time
A. pathology may emerge'in a typical industrialized society that indi-
viduals who are capable of acquiring a substantial volume of physical
inputs, experience a decrease in psychological inputs. As a result,
the level of QOL they produce is declined, as indicated by the switch
of the iso-quality curves from Q^ to Qo in Diagram 6. Note that the
expansion path N N, is downward-sloping in this case.
Diagram 6
PS/time
x1 PH/time
49
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In summary, we have developed a micro quality of life production model
on the assumption that rational individuals are always attempting to
maximize their level of quality of life subject to their own capability
constraints. As conceptualized and analyzed earlier, the quality of
life is not only a function of material well-being, but also dependent
on such nonmaterial or spiritual factors as psychological health, sub-
jective feelings, etc. It has been illustrated that both physical and
psychological inputs can, to a certain extent, substitute for each
other and vary in proportion to produce a given level of QOL.i^/
The assumptions employed under the normal situation are that the
marginal technical rate of substitution is diminishing and that the marginal
contribution of factor input is positive, but diminishing, given other
things being equal. Thus, an increase in both inputs should yield a
higher level of QOL. A "good" social system which enhances its member's
capability to meet his basic and psychological needs is one which con-
stantly helps pushing onward the capability constraints for all its
members. To be specific, a good society is one whose objective is to
ensure the maximum of the iso-capability curves for all individual members
for any given point in time and to shift the curves upward to the right-
hand side over periods of time.
It should be clear now that an increase in GNP alone or sheer stress
on economic growth at the expense of some factor input in the psycholog-
ical side may degrade the QOL in the country. As shown in Diagram 7,
the shift in the iso-capability curve from xy to x'y' means a relatively
smaller sacrifice in PS input but a considerable increase in the PH
input. However, the overall QOL for the nation is adversely affected,
and the level of social well-being is lessened from Q^ to Q (from
equilibrium point N to N1). Unless the sacrifice is compensated for
by a very substantial gain in PH input, say, from ox to OX, people will
then feel indifferent and stay on the same iso-quality curve, Q^.
IT/ In the structure of psychological well-being, Bradbum assumed that
following Herzberg, Mansner, and Snyderman, psychological well-
being is a function of two dimensions--positive and negative
effect, each of which is related to well-being by an independent
set of variables. See Norman Bradbum, The Structure of
Psychological Well-Being (Chicago: Aldine Publishing Company,
1969), and F. Herzberg, B. Mausner, and B. B. Snyderman, The
Motivation to Work (New York: Wiley, 1959).
50
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With the equilibrium point moved from N to P, the gain in economic
well-being by an .amount of xX, for an example, is just enough to cover
the costs of the resulting environmental damage of, say, yY.—'
This study outlines a framework to quantify the quality of life in U.S.
metropolitan areas by measuring the QOL inputs, especially the PH in-
puts for which most data are available. Data on PS inputs are either
not measurable or not existent for all SMSA's. As a proxy for quality
inputs, indexes on some environmental input factors, nevertheless, were
compiled in this study. Ultimately, it is hoped that future develop-
ment in this type of analyses will enable us not only to measure and
evaluate the shifts in the capability curves, but also to identify and
predict the expansion path of the QOL over periods of time under dif-
ferent national goals and policies.
Diagram 7
y
y'
Y
18/ It has been pointed out often enough that environmental pollution
represents a long unpaid debt to nature. It is reasonable to
attribute partially the economic growth in the U.S. since 1946,
to the enlargement of that intangible debt. For this argument,
see Barry Commoner, "The Environmental Costs of Economic Growth,"
in Robert and Nancy Dorfman (eds.), Economics of the Environment
(New York: W.W. Norton and Company, 1972).
51
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CHAPTER IV
MEASURING THE QUALITY OF LIFE IN METROPOLITAN AREAS
The purpose of this study is to develop measures or indicators of the
quality of life in metropolitan areas. Basic concepts and theoretical
issues have been discussed in the previous chapters. This chapter
describes the methodology used to construct our quality of life indi-
cators.
SELECTION AND GROUPS OF METROPOLITAN AREAS
Like a nation, every Standard Metropolitan Statistical Area (SMSA) per-
forms a variety of economic functions, such as production, distribution,
and consumption. Each SMSA may be considered as an economic entity.
Furthermore, each metropolitan area, by definition, has a central city
of at least 50,000 population, and it usually consists of several
neighboring counties of related social, economic, political, and
environmental characteristics. Geographically, the size of a metro-
politan area is approximately traversable by automobile in much less
than a day, i.e., a so-called "commuting distance." From the social
science point of view, an SMSA is an urban area, and most of the
people can complete their social life daily within the metropolitan
area. In addition, all the SMSA's today account for about seven-tenths
of the total United States population. However, social and economic
conditions vary considerably among SMSA's within the country. Under-
standing how and why the quality of life differs among SMSA's seems to
be one of the most important problems in our concerns with society and
with urban pathology. That is to say, one of the substantive tasks in
this quality of life study is to analyze theoretically and test
empirically those variables which significantly determine the variations
in the quality of life among regions.
52
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There were 243 SMSA's in this country in 1970, according to the U.S.
Department of Commerce definition. Although the number of SMSA's has
increased since 1970, and more counties have been added to the defini-
tions of some SMSA's, this study uses the 1970 definition in order to
be consistent with other economic, political, and social data from the
1970 Population Census. These 243 SMSA's had 139.4 million residents
or 68.6 percent of the total U.S. population in 1970. Their populations
range from 56,000 in Meriden, Connecticut, to 11,529,000 in New York City,
New York. From the analytical point of view, it seems to be desirable
to compare the quality of life between SMSA's with comparable population
sizes. Thus, in this study the 243 SMSA's are divided into three groups
according to population: large, medium, and small. SMSA's with
populations greater than 500,000 are in the first group; the small
group includes all SMSA's with population less than 200,000; and the
medium group has populations between 200,000 and 500,000. Although the
total population within the three groups is overwhelmingly high for
the large SMSA's, the numbers of SMSA's in each group are fairly even.
There are 65 large SMSA's with a total population of 102.6 million; about
24.9 million and 11.9 million people live in the 83 medium and 95 small
SMSA's, respectively. These three groups are referred to as Groups
L, M, and S throughout this study.
THE QUALITY OF LIFE FACTORS AND DATA SOURCES
The physical inputs of the overall quality of life consist of five
principal goal areas or QOL components. They are defined in broad
terms, and cover most major concerns of all individuals:
1. Economic Component;
2. Political Component;
3. Environmental Component;
4. Health and Education Component; and
5. Social Component.
These concerns have been chosen with a view to developing as broad and
common as possible a concept of well-being. Psychological inputs are
not included because they are not amenable to quantification. The five
goal areas encompass command over private goods and services being
produced and consumed, and--in addition--the public counterparts not
provided at "market prices" or consumed on an individual basis. The
53
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physical input factors selected in this study tend to possess the
following characteristics:
* They should be sufficiently universal so that the fundamental prin-
ciples would generally be agreed upon by, and apply to, the majority
of people in the metropolitan areas today; they should be of great
present and potential interest to all levels of government as
essential elements of well-being.
* They should be commonly understood and have policy bearings which
can be realistically and efficiently implemented.
* They should be flexible enough to account for any lifestyle input
variations over space and time, and easily adaptable to changes in
social, economic, political, and environmental conditions in a
dynamic society.
* They should be open to verification according to recognized scientific
approaches, and updative with new data so that intertemporal com-
parisons can be made over time.
The number of variables selected under the five goal areas total more
than 120. Insofar as possible, they are formulated in a. way as to
show both the concerns of the individual and the well-being of the
community. The interdependent relationship among variables is also
recognized; the same variable may appear simultaneously in two different
goal areas, and yet the independent objective among the five principal
goals is fundamentally unaffected.
The variables selected for the study in their respective order of sequence
are discussed below. As shown in Panel 1, page 57, the sign on the left
of each variable indicates the effect of the variable on the quality of
life—the positive or negative contribution to the input measurement.
Economic Component
The economic inputs to the quality of life are divided into two cate-
gories: individual economic well-being and community economic health.
Personal income and wealth status are considered to be the most sensitive
indicators of economic well-being of individuals. Personal income repre-
sents the flow variable; the wealth reflects the stock. On an individual
basis, a metropolitan area with a higher stock of wealth and a larger
flow of incomes tends to be healthier than those with lower wealth and
smaller incomes, ceteris paribus.
54
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The wealth status of an individual can generally be measured by his
fixed assets, properties, and changes in income. In this study, the
median value of owner-occupied single family housing, the percentage
of owner-occupied housing units, and the percent of households with
one or more automobiles are used to reflect wealth status. Savings
per capita are employed to represent the variable assets and the ratio
of total property income to total personal income as an index of
property as cumulation and production. They all are positive inputs
to the wealth category, and hence,become positive inputs to the
quality of life.
There are seven factor inputs to a community's economic health; these,
coupled with an individual's economic well-being, constitute the
economic component. Affluency, employment, labor productivity, indus-
trial diversification, availability of capital and the community's
economic development efforts are all essential inputs to strengthen
a community's economic health. In addition, a more and more even dis-
tribution of economic resources among people is gradually expected for
a healthy economy. For this,the inequality index between central city
and suburban income is also selected to measure the economic health of a
community. Income inequality and unemployment rates are the negative
input factors, while the remaining five are positive attributes to the
metropolitan economy.
The inequality between central city and suburban income distribution
is one of two factors used in determining the income inequality index.
Urban blight has become one of the critical metropolitan issues. The
distribution of population and income between the central city and the rest
of the SMSA is examined in a review of this factor, whereas the other
factor centers on the percentage of persons with either high or low
incomes within the SMSA as a whole. The distribution of total income
between the persons in the central city and suburban part of the SMSA
identifies the inequalities which may exist. The equation used in
calculating this factor is a reduced version of the Gini coefficient,
or is:
2(P - P
Cp Cy
where Pcp is the percentage of population living in the central city and
Pcy is the percentage of total income that is distributed in the central
city. The ideal situation would be a perfect equality, or for both of
these percentages to be equal; hence, the greater the deviation from
zero, the less favorable the distribution.
55
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The degree of economic concentration is expressed by the percentage of
persons employed in the manufacturing and services industries in the
SMSA's as compared to the corresponding figures for the U.S. as a
whole. "Services" is defined by the U.S. Department of Commerce as
business, repair, and personal services. The equation used in calculating
this factor is as follows:
with BJJJ and eg defined as the percent of total employed in manufacturing
and services in the SMSA and E^ and E the corresponding totals for
the U.S. Since a diversified regional economy is less vulnerable than
a highly concentrated one when the national economy changes its
structure or suffers from any unavoidable or uncontrollable conditions,
the variable should be viewed like the inequality variable: i.e.,
the greater the deviation from zero, the less favorable the structure.
In summary, the economic component of the metropolitan quality of life
is represented by 18 individual and community inputs ranging from in-
come and wealth to economic concentration and income distribution.
(See Panel 1). All selected variables are deemed as physical inputs
that produce a certain level of quality of life under study regardless
of their conventionally conceived input or output characteristics. In
other words, they may jointly reflect a capability or command over
goods and services of the metropolitan population that might have been
differentiated otherwise. Moreover, all variables, be they individual
or community concerns, depict not only the most essential fields of
economic component in this country, but also the most critical area of
today's political and welfare economy among metropolitan regions.
Statistics for-those variables shown in Panel 1 are mainly collected
from the Census of Population, 1970, (COP), County and City Data Book.
1972 (C and C), U.S. Statistical Abstract, 1972 (SA) Census of
Government, 1967 (COG), etc. The Appendix contains raw data and data
sources for all variables employed in this study.
56
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Panel 1. FACTORS IN ECONOMIC COMPONENT
Factor Effect
and Weight Factors
I. Individual Economic Weil-Being
+ (.25) A. Personal income per capita ($)
B. Wealth
+ (.05) 1. Savings per capita ($)
+ (.05) 2. Ratio of total property income to total
personal income
+ (.05) 3. Percent of owner-occupied housing units
+ (.05) 4. Percent of households with one or more
automobiles
+ (.05) 5. Median value, owner-occupied, single family
housing units ($1,000)
II. Community Economic Health
+ (.07) A. Percent of families with income above poverty level
(.07) B. Degree of economic concentration, absolute value
C. Productivity
+ (.014) i^ Value added per worker in manufacturing ($1,000)
+ (.014) 2. Value of construction per worker ($1,000)
+ (.014) 3. Sales per employee in retail trade ($1,000)
+ (.014) 4. Sales per employee in wholesale trade ($1,000)
+ (.014) 5. Sales per employee in selected services(?1,000)
+ (.07) D. Total bank deposits per capita ($)
E. Income inequality index
(•°35) 1. Central city and suburban income distribution
(.035) 2. Percent of families with incomes belofo poverty
level or greater than $15,000
— (.07) F. Unemployment rate
(.07) G. Number of full-time Chamber of Commerce employees
per 100,000 population
57
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The following two variables have special definitions and connotations:
The savings per capita variable includes only deposits in savings and
loan associations, and excludes savings accounts in banks or other
institutions. The amounts shown for the SMSA's are the totals for all
savings and loan associations headquartered in that area, including
their branches located elsewhere.
The number of full-time employees of the Chamber of Commerce was obtained
by means of a questionnaire sent by MRI to the Chamber located in the
central city of the SMSA. (The questionnaire form is contained in the
Appendix.) Therefore, the information presented is only for the central
city and is used as an approximation to the entire SMSA. Estimates were
made for the Chambers that either did not return the questionnaire or
did not fully complete it. Estimates for the large SMSA's were based
on SMSA's of comparable population size. For the medium and small sized
SMSA's, the minimum value of the SMSA's available in each group was
used as a basis for estimation. As shown in the Appendix, all estimated
figures are marked with a dot behind them.
Political Component
While variables in the economic component are designed to measure either
the command over goods and services or the capability to satisfy the
basic needs for a decent standard of living of all the population
within each metropolitan area, the political component is intended to
describe the institutional factors and the functional operations of the
democratic system which organize all individuals in a community to achieve
some common goals and public objectives. The goals and objectives are
determined collectively, and their products are characterized by the
nature of nonmarketability, indivisibility, and relevant externalities.
Within the political arena, two types of factors are considered as vital
inputs to the metropolitan quality of life. One is the professionalism
and performance of the local governments and the other is individual
activities. The most important input of any individual is undoubtedly
his own active participation in political events. The ratio of presi-
dential votes cast to voting age population is selected as the variable
with available data that best represents individual participation.
Individuals have to be well informed so that they can be well prepared
and equipped for action or participation. Newspapers and radio and
television broadcasts are most efficient communication media for
the public in general and for governments in particular. Thus, they
are selected to represent the informed citizenry and should have direct,
positive effects upon the political quality of life of individuals.
58
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The collective policies have to be implemented, and the public goods
and services have to be provided for metropolitan residents by the
governments. The quality of governments may be judged from the pro-
fessionalism and number of employees, while efficiency or accomplish-
ment may be reflected by the level of output or performance. The
qualification and number of teachers, policemen, and firemen employed
are by far the most conventional indicators of professionalism in
state and local government. Eight variables are chosen for this
category. Crime prevention is the most tangible and sensitive criterion
when local governments are evaluated. The existence of high violent
crime and property crime rates are indicators of poor government
performance and detrimental to our quality of life. The willingness
to finance production and to maintain the quality of these public goods
and services is directly illustrated by the local government revenue
per capita. The local governments are described as more efficient if
they can secure more funds from the Federal Government.
In addition to the crime rate, community health and educational results
are also good indicators of government performance, and constitute
significant inputs to the quality of life. Therefore, although these
two community indexes are computed under the health and education
component, they also appear under the political component. This is
one of the cases in which the interdependent relationship among
variables manifests itself.
Public welfare payments and welfare assistance from the state and local
governments are considered another important role of the political
mechanism. With the Federal Government's emphasis on equal opportunity,
the welfare assistance helps to assure a minimal level of living
standard for all who are incapable or needy. As a result, the welfare
variables are included to measure the degree to which the public
provisions for the basic needs are generally extended.
The 21 variables shown in Panel 2 are by no means complete, and are not
intended to be. However, they reflect the overall concerns of our
political quality of life, and the yardsticks for them can be established.
Consequently, related policies leading toward improvement can be designed
and recommended. Except for the crime variables, they all are positive
input factors in the model of quality of life production.
59
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Panel 2. FACTORS IN POLITICAL COMPONENT
Factor Effect
and Weight Factors
I. Individual Activites
A. Informed citizenry
+ (.083) 1. Local Sunday newspaper circulation per 1,000
population
+ (.083) 2. Percent of occupied housing units with TV
available
+ (.083) 3. Local radio stations per 1,000 population
+ (.25) B. Political activity participation-ratio of Presiden-
tial vote cast to voting age population
II. Local Government Factors
A. Professionalism
+ (.02) 1. Average monthly earnings of full-time teachers ($)
+ (.02) 2. Average monthly earnings of other full-time
employees ($)
+ (.02) 3. Entrance salary of patrolmen ($)
+ (.02) 4. Entrance salary of firemen ($)
+ (.02) 5. Total municipal employment per 1,000 population
+ (.02) 6. Police protection employment per 1,000 population
+ (.02) 7. Fire protection employment per 1,000 population
+ (.02) 8. Insured unemployment rates under state, federal,
and ex-servicemen's programs
B. Performance
(.03) 1. Violent crime rate per 100,000 population
(.03) 2. Property crime rate per 100,000 population
+ (.03) 3. Local government revenue per capita
+ (.03) 4. Percent of revenue from federal government
+ (.03) 5. Community health index
+ (.03) 6. Community education index
C. Welfare assistance
+ (.053) !• ?er capita local government expenditures on
public welfare ($)
+ (.053) 2. Average monthly retiree benefits ($)
+ (.053) 3. Average monthly payments to families with
dependent children ($)
60
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All data sources are detailed in the Appendix. Because of the paucity
of comparable statistics for all metropolitan areas, however, many
variables under this political component are substituted by close
approximations. They are explained as follows.
Local Sunday newspaper circulation per 1,000 population measures the
Sunday circulation of newspapers based in the central city of the
SMSA. However, this figure may include areas outside the central city,
and in some cases outside the SMSA itself. Local radio stations per
1,000 population include only the radio stations located in the
central city of the SMSA. It therefore excludes stations which may be
located either in the suburbs of the central city or perhaps in other
SMSA's.
The 1973 Statistical Abstract contains the number of votes cast in
the 1972 Presidential election for SMSA's with a population of more than
200,000. Information for the 1968 Presidential election was available
for the smaller sized SMSA's in the County and City Data Book, 1972.
The minimum voting age used to compute the ratio of Presidential vote
to voting age population was 21 in all states except Georgia (18 years),
Kentucky (18 years), Alaska (19 years), and Hawaii (20 years) for the
1968 election. In 1971, the voting age was lowered to 18 years in all
states with the adoption of the 26th Amendment. Since voter regis-
trations are kept by county, data for Standard Economic Areas (SEA) were
substituted for the SMSA's in New England. In a few cases state data
were also used for SMSA's.
The average monthly earnings of full-time teachers and other full-time
employees were obtained from the Census of Government, Volume 5.
However, where data were not available for an SMSA, state average data
were used. The entrance salaries of patrolmen and firemen refer to that
earned during the first 12 months on duty. The data shown are for
the central city of the SMSA. , The median entrance salary of all central
cities was used if information] was not available for the central city
of the SMSA.
Since there are no comparable data on municipal employees for an entire
metropolitan area, the total number of full-time municipal employees
per 1,000 population in the central city of the SMSA is used as a
substitute. The police and fire protection factors include full-time
uniformed forces, administration, and clerical personnel.
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The Manpower Report of the President contains unemployment data for
150 major labor areas as well as for states. The insured unemployment
rates under state, federal, and ex-servicemen's programs show the in-
sured unemployment as a percent of the average covered employment for
the areas. State data were substituted if data for the major labor
area were not available for a particular SMSA. Because data for the
smaller sized SMSA's were very limited, this variable was omitted
from the study for the small SMSA's.
Violent crime is defined as offenses of murder, forcible rape, robbery,
and aggravated assault; property crime is offenses of burglary, larceny
of $50 and over, and auto theft. The FBI Uniform Crime Rates for the
United States contains these crime rates for SMSA's. County data were
gathered in place of these rates in the New England states, so SEA's are
shown instead of SMSA's. In other instances, state data were the only
available source of information.
Percent of revenue from Federal Government and local government revenue
per capita were taken from local government data as found in the
Census of Government. State data were used if SMSA data were not
available. Public assistance payments, recorded by county, were
aggregated to obtain SMSA figures. Information for the New England
SMSA's is actually SEA data. The state average was again substituted
if no county data were available.
Environmental Component
We are told frequently that human values and institutions have set
mankind on a collision course with the laws of nature. It is not yet
clear precisely when and in what form the collision between economic
growth which can satisfy many human wants, and natural limits will
occur, but the recent energy shortage vividly ^signals the onrush
of crises and environmental problems. The environment is the unique
skin of soil, water, gaseous atmosphere, mineral nutrients, and
organisms which, powered by the energy of the sun, make Earth hospitable
to human life. We have long learned to modify and to exploit the en-
vironment to our advantage in numerous ways, yet we still cannot claim
either full understanding or control of the environmental systems that
support our growing population. Not until fairly recently did environ-
mental protection and natural resource conservation become focal points
of public interest and national concern in this country.
The environmental component in this study ideally should take into
account factors other than pollution, climate, and recreational facilities
62
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such as natural endowments and conservation, resource availability
and accessibility, etc. However, the scarcity of comparable data
for SMSA's prevents those representative variables from being selected
and included. Thus, the environmental variables affecting the metro-
politan quality of life encompass only the air, visual, noise, solid
waste and water pollution, climatological and recreational factors.
All types of pollution are grouped under the individual and institu-
tional environment because they are different by-products of various
human activities. Evidence suggests that the direct effects of
pollution on property, on human health, and on the quality of life are
varied. Their direct damages, however, may ultimately prove to be
even less critical for society as a whole than the latent effects of
pollution on the ecological systems that sustain human life.-
The natural environment component includes five climatological variables
and two recreational variables: sunshine days, inversion frequency,
thunderstorms, high and low temperatures, areas of parks and recrea-
tional areas, and miles of trails. Parks and recreational areas have
come to play an ever-increasing, important role in our city life.
As a result, this variable is used twice in the environmental component,
serving as a determinant of visual pollution and a factor of natural
environment as well (see Panel 3).
All variables, except the parks and recreational areas, miles of trails,
and sunshine days, in this section have adverse effects on our environmental
quality, and are negative inputs to our daily life. Thus 17 variables
shown in Panel 3 depict mostly our urban environmental "bads" rather
than "goods." They are chosen for the following reasons: making us
alert to our environmental problems, comparing the cleanliness of our
environment, and judging the efforts made to reduce and eliminate
the pollutants.
The air pollution index is comprised of two factors—total suspended
particulate levels and sulfur dioxide levels. The information provided
for total suspended particulates is the 1972 geometric mean level.
I./ For detailed discussion, see P. R. Ehrlich, A. H. Ehrlich, and
J. P. Holdren, Human Ecology (San Francisco: W. H. Feeman and
Company, 1973); Larry B. Barrett and Thomas E. Waddell, Cost of
Air Pollution Damage: A Status Report (Research Triangle Park,
North Carolina: National Environmental Research Center, 1973), and
Thomas E. Waddell, The Economic Damages of Air Pollution
(Washington, D.C.: EPA Washington Environmental Research Center,
May 1974).
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Panel 3. FACTORS IN ENVIRONMENTAL COMPONENT
Factor Effect
and Weight Factors
I. Individual and Institutional Environment
A. Air pollution index
(.05) 1. Mean level for total suspended particulates
- (.05) 2. Mean level for sulfur dioxide
B. Visual pollution
(.033) 1. Mean annual inversion frequency
(.033) 2. Percent of housing units dilapidated
"*" (.033) 3. Acres of parks and recreational areas per
1,000 population
C. Noise
(.033) 1. Population density in the central city of the
SMSA> persons per square mile
(.033) 2. Motor vehicle registrations per 1,000 population
(.033) 3. Motorcycle registrations per 1,000 population
(•10) D. Tons of solid waste generated by manufacturing per
million dollars value added
(.10) E. Water pollution index
II. Natural Environment
A. Climatological data
/ 05) 1. Mean annual inversion frequency
+ / Q5\ 2. Possible annual sunshine days
(.05) 3. Number of days with thunderstorms occurring
(.05) 4. Number of days with temperature of 90° and above
(.05) 5. Number of days with temperature of 32° and below
B. Recreation areas and facilities
+ (.125) 1. Acres of parks and recreational areas per
1,000 population
+ (.125) 2. Miles of trails per 100,000 population
64
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The 1972 arithmetic mean level is shown for sulfur dioxide in the larger
sized SMSA's, but due to data deficiencies the maximum observation is
shown for the medium sized SMSA's. Estimates were made for some of
the SMSA's where no Federal Air Monitoring Site was located, and hence
no pollution concentrations were recorded. The air pollution informa-
tion relates only to the central city of the SMSA, and where the data
were not available, estimates were based on the central city of a
neighboring SMSA. Information for the smaller sized SMSA's was
extremely limited and therefore, omitted from the study for small SMSA's.
The frequency of low-level inversion (stable air) is an important
factor of visual pollution. The data were obtained from the Air
Quality and Emissions Trends Annual Report which includes a map showing
the percent of total hours with inversions based 150 meters or less
above the ground for the U.S. The map reflects the influences of
mountains, lakes, and oceans on this factor.
Motor vehicle and motorcycle registrations are recorded by the Department
of Transportation by county. Registration data for cities and towns
were not available, so the data for SMSA's in the New England states
are again SEA data. Where neither SMSA nor SEA data were available,
estimates were made based on the average of the SMSA's in the state,
census division, or census region, depending on the availability of
data.
Solid waste generated in the manufacturing industry was obtained by
multiplying a factor of 7.6 tons by the total number of employees in
the manufacturing industry in the SMSA in the year 1970.—' This figure
was then divided by the value added by manufacturing (in million
dollars). For SMSA's where value added information was either not
available or was withheld to avoid disclosure, the state average figures
were substituted.
A water pollution index based on the prevalence, duration, and intensity
of pollution has been developed for all SMSA's by the Mitre Corporation,
and is called the PDI index. A lower PDI rank indicates a worse
pollution problem. The figures shown for the water pollution index are
the PDI rank for all Basic Data Units (BDU's) in the U.S. divided by
the corresponding SMSA value. This was done so that the lower values
reflect less of a water pollution problem. State values were sub-
stituted where SMSA values were not available.
"if This is the waste multiplier used in J. L. Berry et al., Land Use,
Urban Form and Environmental Quality (The University of Chicago,
Department of Geography Research Paper, Number 155, 1974), p. 268.
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The U.S. Department of Commerce presents climatologlcal data for cities
in an annual publication called Local Climatological Data. The figures
for possible annual sunshine days represent the number of hours of
sunshine as a percent of the number of hours between sunrise and sunset
for each day of the year. The number of days with thunderstorms
occurring and the maximum number of days with high (90° and above) or
low (32° and below) temperatures are statistics for the weather stations.
Data were not available for all of the central cities of the SMSA's,
so observations for nearby stations having approximately the same
climatic conditions were substituted in some cases.
The statistics for all parks and recreational areas, trails, etc., in
this study were obtained from the 1972 Public Outdoor Recreation Areas
and Facilities Inventory Survey conducted by the Bureau of Outdoor
Recreation. Statistics are available at the county level, and the
county data were aggregated to obtain SMSA information. Estimates
based on the state totals were used for the SMSA's where no information
was available.
Health and Education Component
The quality of health and education is another principal concern. Three
major health considerations have been identified as dominating factors,
i.e., long life, life free of disability, and medical care availability
and accessibility. Long life reflects the human desire to live out a
natural life span, which means a low death probability at every age in
the life cycle. It is conventionally measured by life expectancy at
birth, or the average life expectancy. However, life expectancy at
birth depends substantially on the infant mortality rate, and sub-
sequently on the average death rate. For this reason, the infant
mortality rate and the death rate are employed in the study to measure
individual health condition.
While no specific variable was chosen for life free of disability, due
to data deficiencies, the availability of and accessibility to medical
care are employed to reflect the conditions of community health pro-
tection. Disability can be partly prevented if quality medical care
services are provided when needed. The number of physicians and
dentists per 100,000 population represent the availability of medical
manpower, and the number of hospital beds indicates the facili-
ties. The accessibility of medical care can probably be reflected by
per capita local government expenditures on health. Although the
hospital occupancy rate is undoubtedly an indicator of efficiency
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and utilization, it may possibly reflect accessibility—hospital
occupancy rate can be higher in one area than another only if patients
in the area have better access to the hospitals than those in the
other, given that the demographic characteristics, health conditions,
number of hospital beds, and everything else are the same in both
areas.
The achievement of a basic level of education among residents and the
opportunity for higher, better, and continuing education in a community
are the primary concerns of today's intellectual health. Attaining a
basic level of education implies that all persons, especially youth,
have developed or been equipped with those essential skills required
to participate and contribute in society independently, and to pursue
their own interests and self-satisfaction intelligently. The existing
opportunities and the willingness to invest in formal education or
vocational training, whether for better employment opportunities,
individual dignity and independence, or other general interest pursuits,
are important community conditions for a healthy educational climate.
Furthermore, personal relationships in a community are likely to be
more harmonious and better communicated if educational backgrounds and
the intellectual drives within the community are relatively homogeneous.
For individual educational attainment, the median school years completed
by persons 25 years old and over, and the percentage among them with
4 years of high school or more, are selected as positive indicators.
The percent of males between 16 and 21 years of age who are not high
school graduates is considered as a negative indicator affecting
educational homogeneity; the percent of population, ages 3 to 34,
enrolled in schools is chosen as a positive indicator of individual
willingness to invest in education. The willingness of a community to
invest in education is shown by the variable of per capita local
government expenditures on education, whereas a community's educational
attainment and probably its homogeneity are illustrated by the percent
of persons 25 years old and over who have completed 4 years of college
or more.
The 13 factors described above are expected to portray, respectively,
the individual and community conditions of health and education
needed to evaluate the level of quality of life in the metropolitan
areas. The policy implications of the health variables are that the
overall social well-being is improved if life expectancy is lengthened,
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Panel 4. FACTORS IN HEALTH AND EDUCATION COMPONENT
Factor Effect
and Weight Factors
I. Individual Conditions
A. Health
- (.125) !• Infant mortality rate per 1,000 live births
- (.125) 2. Death rate per 1,000 population
B. Education
+ (.063) !• Median school years completed by persons
25 years old and over
+ (.063) 2. Percent of persons 25 years and over, who
completed 4 years of high school or more
- (.063) 3. Percent of males ages 16 to 21 who are not
high school graduates
+ (.063) 4. Percent of population ages 3 to 34 enrolled
in schools
II. Community Conditions
A. Medical care availability and accessibility
+ (.05) 1. Number of dentists per 100,000 population
+ (.05) 2. Number of hospital beds per 100,000 population
+ (.05) 3. Hospital occupancy rates
+ (.05) 4. Number of physicians per 100,000 population
+ (.05) 5. Per capita local government expenditures on
health
B. Educational attainment
"*" (.125) ^* **er caPita local government expenditures on
education
•*• (.125) 2. Percent of persons 25 years old and over who
completed 4 years of college or more
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and more and better medical care services are made available and
accessible. The policy implications of the educational variables are
that quality of life can be enriched by increasing both public and
private investment in education and stressing uniform educational
attainment among individuals.
All educational variables contained in Panel 4 are found in the census.
The infant mortality rate and death rate are based on information ob-
tained from certificates filed in state or city Bureaus of Vital
Statistics. Thus, this information is limited to registered occurrences
only. Again, SEA data were used for the New England SMSA's; state
data were substituted for SMSA in a few instances when no SMSA data
were located.
Limitations of data existed for the five factors comprising medical
care availability: the number of dentists and physicians was not
available in the Statistical Abstract for SMSA's with populations of
less than 200,000. As a result, these variables are not included for
the small SMSA groups.
Social Component
Insofar as the quality of life is conventionally defined as social
well-being and measured by social indicators, the social component
constitutes the most significant and important element of this study.
Due to the wide range of social concerns, a relatively larger number of
factors are included in the social component. These variables depict
primarily three central social issues: individual concerns, individual
equality, and community living conditions.
Among the individual concerns in the social component, the quality of
life is identified with the opportunity for self-support, the promoting
of maximum development of individual capability, and a widening oppor-
tunity for individual choice. The concern with self-support implies inde-
pendence and. self-reliance. The existing opportunity for self-support
thus may be represented by the labor force participation rate, the per-
cent of labor force employed, the mean level of income which reflects
employment and income earning opportunity, the family status of the de-
pendent children, and the independence of married couples. Education,
as described previously, provides essential skills needed to acquire em-
ployment, and also more often than not education generates employment
opportunities. Therefore, it is also included to identify the existing
opportunity for self-support. For the development of individual
capabilities in this country, no investment other than education can be
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formal, efficient, effective, and rewarding. For persons with less
than 15 years of education, some vocational training apparently enhances
their capabilities professionally. Physically, health is fundamental
to any development of individual capability. Thus, the individual health
index also becomes one of the essential determinants in this group;
i.e., the index values, after they are computed, are included in this
subcomponent.
Individuals are expected to be very much concerned with available choices
and appreciative of chances to acquire better knowledge and information
about selection among jobs, residences, friends, etc. In order to
widen opportunity for individual choices, individuals have to be
mobilized with better transportation, and information has to be
broadly distributed and timingly expedited. To assure mobility and
efficient communication, variables such as automobile registration,
newspaper circulation, and television and radio stations are used as
positive indicators. The mobility and spatial choices are limited for
young and senior citizens in the central city, and these limitations
are probably the more serious the higher the population density. In
addition, individual equality seems to be one of the preconditions for
widening individual choices which,in turn, are obviously affected by
the individual and institutional environment delineated previously.
Individuals are born equal and are concerned about racial, sex, and
other discriminations. Regardless of race, sex, religion, and location,
people in this country are protected by the law to enjoy equally the
educational and employment opportunities that exist. Discrimination,
however, is still present in this country due to reasons other than
education. To reveal the rate at which racial and sex discrimination
are being gradually eliminated within the metropolitan areas, the
income and employment differentials between nonwhite to total persons,
between nonwhite males to total males, between nonwhite females to
total females, and between males to females, are all adjusted by the
level of education and presented under the individual equality criterion.
The implication of these variables is that the higher the equality,
and the less the discrimination not resulting from educational
differences, the better the quality of life.
Four factors comprising racial differences identify the inequalities
that may exist between Negroes and the total number of persons in the
SMSA. The ratios of median family income, professional employment,
and the male and female unemployment rates are adjusted for the
different education levels of Negroes and total persons. The median
family income and professional employment ratios are computed as follows:
70
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Negro data ... Educational level of total persons
•^^««^^—^™- •• • " '• • »• I "^^™«» Jt Hill I I - I •••! .1 • .-... ^ I I * • •
Total persons data Education level of Negroes
The education level is the median number of school years completed.
The unemployment rate ratios are computed in basically the same manner,
i.e.:
Negro data Education level of total persons _ j_
Total persons data Education level of Negroes
The ideal situation would be for no inequalities to exist, in which
case the factors would have a value of 1.0. For certain SMSA's the
number of Negroes was so small that information was not available.
In these cases a value of 1.0 was used.
Differences between male and female unemployment rates and numbers
professionally employed are clearly evident. The method used to compute
the male to female ratios is similar to the one described above for
Negroes and total persons. The formula is as follows:
Male data x Education level of females _ •>
Female data Education level of males
Again, the ideal situtation is a value of 1.0, while in most cases it
is smaller. Three spatial variables are considered as negative
attributes to the equality consideration. A high percentage of people
working outside county of residence generally indicates that the
surrounding counties benefit substantially from incomes earned in
the central city, while the central city, after providing job opportuni-
ties and public services, is significantly suffering from property
tax revenue losses. Moreover, the commuters are normally in high paying
jobs in the central city of an SMSA. As a result, the income inequality
problem between those in the central city and others in the rest of
an SMSA tends to be aggravated over periods of time. The third concern
is the housing segregation problem. A housing segregation index which
measures the percentage of Negroes living in the central city,as com-
pared to the SMSA as a whole, is constructed. The formula used in
computing this index is as follows:
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Percent of Negroes living in central city _
Percent of Negroes living in SMSA
Values closer to zero are considered to represent a good balance in
the SMSA, and hence, the quality of life.
The last of the critical social concerns in this study is community
living conditions. These conditions circumscribe our daily life, and
everyone's quality of life is vitally affected by them. Among the
conditions three major areas are studied and variables pertaining to
these three are selected. They are general living conditions, facilities,
and other social conditions.
Within the general living conditions category, factors of great concern
are community poverty, decent housing and living space, adequate
utility services, uses of public transportation, crime rate, and the
cost of living. While most of the data for the preceding variables
are available in the Census of Population, a special endeavor was made
to construct the cost of living index. They are computed on the basis
of the American Chamber of Commerce Researchers Association (ACCRA1 s)
"Intercity Index Report" on the cost of living. The report, however,
included indexes for only 105 central cities of the 243 SMSA's. The
others were estimated according to the following formula:
n
- °'35 Xa C1 ' VRa>
where In and Ia are, respectively, the indexes for an SMSA where an
ACCRA index is not available and for a neighboring SMSA with ACCRA
data, and ^ and Ra are the median gross rents for the two SMSA's.
The 0.35 represents the fact that rent was given a weight of 35 percent
in the computation of the cost of living index by the ACCRA. The
indexes are for the central cities in the SMSA's.
Under the facilities category, indicators representing public recreational
facilities, financial institutions, service and trade establishments,
hospitals and libraries are employed. As mentioned in the Environmental
Component, data on recreation were surveyed by the United States Bureau
of Outdoor Recreation and are incomplete as might be expected. The
number of swimming pools, camping sites, tennis courts, and the miles
of trails reported may, therefore, be much lower than is actually true
for the SMSA's. Only public facilities are included, which may exclude
a large number of private facilities in some SMSA's. Estimates based
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on the state totals, or based on the minimum value of the SMSA's
available in each size group, were used for the SMSA for which no in-
formation was available.
The total number of banks and savings and loan associations located in
each SMSA is given in the Statistical Abstract, Section 33. However,
information was not provided for the SMSA's with population less than
200,000. The volumes of books in the main public library per 1,000
population includes only the volumes of books which are shelved in
the main public library of the central city in each SMSA. Data for
university of other libraries located in other parts of the SMSA are
not included. Limitation of data was the only problem encountered in
computing the number of trades and services establishments. Where informa-
tion for the SMSA data was not available, the state figure was sub-
stituted.
All the facility variables are positive inputs of our urban life; their
availability and the accessibility to those public facilities and
commercial establishments are primary social concerns to every
metropolitan resident.
In addition to the general living conditions in the community that
persons in the community jointly participate in and collectively enjoy,
there are special cultural, sports, and other social activities.
While it is generally agreed that the more sports and cultural activities,
the higher the community health, education and natural environment indexes,
and the lower death rate, the better is the quality of social life,
the negative contribution of birth rate may warrant some explanation.
It is hypothesized in this study that the majority of the population in
this country is in favor of family control, and that the zero rate
of population growth is also a social goal. All birth and death rates
are based on original certificates filed in state and city Bureaus of
Vital Statistics, and therefore include only registered occurrence.
Information on both sports and cultural events was obtained through
the questionnaire sent by MRI to the Chamber of Commerce in the central
city..?./ The sports category includes five major sports (football, base-
ball, basketball, hockey, and soccer). Each item is given points
based on the class of team which played on a regular seasonal basis
in the central city. Major league teams are given 3 points; minor
league, 2 points; and college or university teams, 1 point. A
maximum of 30 possible points is possible. The dance, drama, and
music events factor includes the following 12 areas: ballet, modern
3/ The questionnaire forms are contained in the Appendix.
73
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dance, folk/ethnic dance, plays, stage productions, opera, symphonic/
philharmonic, chamber music groups, choirs, country/western/bluegrass
rock concerts, and jazz. Again, SMSA's are given points depending upon
the type of event held regularly--professional, 3 points; semiprofes-
sional, 2 points; university, college or touring groups, 1 point.
The maximum here is 84 points. Cultural institutions include art,
science, history, and natural science museums located in the area.
The number and importance of fairs and festivals held are rated in the
following manner. Fairs or festivals of national importance are given
3 points; regional events, 2 points; and local, 1 point.
Of the total questionnaires sent to the 243 Chambers of Commerce in the
large (65), medium (83), and small (95) SMSA's, there were, respectively,
51, 69, and 77, or a total of 197 (81.1 percent) returned in time
for compilation. Some questionnaires were received too late to be
included. The minimum values of the returned questionnaires in the
medium and small groups were respectively assigned to those SMSA's whose
Chambers of Commerce failed to respond. For those which did not
respond, the values for the large SMSA's were estimated by taking the
average of other large SMSA's in the same state.
Thus, the Social Component, due to its broad nature and varying perceived
concerns with our social well-being, is comprised of 54 factors. They
are selected primarily according to our criteria set forth in the
beginning part of this section. They are assumed to reflect critical
social issues such as individual equality, individual concerns and
community living conditions, etc. While some variables are repre-
sented by published official sources, some are denoted by the firsthand
data collected and computed by MRI. (See Panel 5.)
In summary, about 125 variables have been selected and described in
connection with the current economic (EC), political (PO), environmental
(EN), health and education (HE), and social (SO) goal concerns. They
all have been considered as important determinants essential to measuring
the quality of life for today's urban population in the U.S. Jointly,
they are expected to represent the physical ingredients or objective
inputs which substantially contribute to the production of a certain
level of the quality of life among the metropolitan areas. The scope
of this study covers a wide spectrum. Under the five main components,
popular issues ranging from individual income and wealth, income in-
equality, political participation, pollution, educational attainment,
and individual equality, to economic structure, government performance,
environmental protection, community investment in education and health,
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Panel 5. FACTORS IN SOCIAL COMPONENT
Factor Effect
and Weight Factors
I. Individual Development
A. Existing opportunity for self-support
+ (.018) 1. Labor force participation rate
+ (.018) 2. Percent of labor force employed
+ (.018) 3. Mean income per family member ($)
+ (.018) 4- Percent of children under 18 years living with
both parents
- (.018) 5. Percent of married couples without own household
"*" (.018) 6. Individual education index
B. Promoting maximum development of individual capabilities
+ (.028) 1. Per capita local government expenditures on
education ($)
+ (.028) 2. Percent of persons 25 years old and over who
completed 4 years of high school or more
3. Persons ages 16 to 64 with less than 15 years
of school but with vocational training
+ (.014) a. Percent of males
+ (.014) b. Percent of females
+ (.028) 4. Individual health index
C. Widening opportunity for individual choice
1. Mobility
+ (.007) a. Motor vehicle registrations per 1,000
population
+ (.007) b. Motorcycle registrations per 1,000
population
+ (.007) c. Percent of households with one or more
automobiles
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2. Information
+ (.007) a. Local Sunday newspaper circulation per
1,000 population
+ (.007) b. Percent of occupied housing units with TV
available
+ (.007) c« I«°cal radio stations per 1,000 population
3. Spatial extension
-(.Oil) a. Population density in SMSA, persons per square mile
-(.Oil) b. Percent of population under 5 and 65+
living in central city
+ (.022) 4. Individual equality index
+(.022) 5. Individual and institutional environment index
II. Individual Equality
A. Race
+ (.028) 1. Ratio of Negro to total persons median family
income adjusted for education
+ (.028) 2. Ratio of Negro to total persons in professional
employment adjusted for education
- (.028) 3. Ratio of Negro males to total males unemployment
rate adjusted for education , absolute value
" (.028) 4. Ratio of Negro females to total females unem-
ployment rate adjusted for education, absolute value
B. Sex
" (.055) 1« Ratio of male to female unemployment rate
adjusted for education, absolute value
- (.055) 2. Ratio of male to female professional employment
adjusted for education, absolute value
C. Spatial
- (.037) 1. Percent working outside county of residence
- (.037) 2. Income inequality index—central city and
suburban income distribution, absolute value
" (.037) 3. Housing segregation index, absolute value
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III. Community Living Conditions
A. General conditions
+ (.016) 1. Percent of families with income above poverty
level
+ (.016) 2. Percent of occupied housing units with
plumbing facilities
- (.016"* 3. Percent of occupied housing units with 1.01
or more persons per room
+ (.016) 4. Percent of occupied housing units with a
telephone available
+ (.016) 5. Percent of workers who use public transportation
to work
- (.016) 6. Total crime rate per 100,000 population
- (.016) 7. Cost of living index
B. Facilities
1. Recreational facilities
+ (.005) a» Number of swimming pools per 100,000
population
"*" (.005) b. Number of camping sites per 100,000
population
"*" (.005) c* Number of tennis courts per 100,000
population
+ (.005) d. Miles of trails per 100,000 population
+ (.018) 2. Number of banks and savings and loan associa-
tions per 1,000 population
"*" (.018) 3. Number of retail trade establishments per
1,000 population
"*" (.018) *** Number of selected service establishments per
1,000 population
+ (.018) 5. Number of hospital beds per 100,000 population
+ (.018) 6. Volumes of books in the main public library
per 1,000 population
C. Other social conditions
- (.018) !• Death rate per 1,000 population
" (.018) 2. Birth rate per 1,000 population
•*• / QIQ) 3. Sports events in the metropolitan area
4. Cultural events in the metropolitan area
+ (.007) a. Dance, drama, and music events
"*" (.007) b. Cultural institutions
"*" (.007) c* Fairs and festivals held
+ (.018) 5. Community health and education index
+ (.018) 6« Natural environment index
77
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transportation, cultural and social activities and a host of urban
problems such as housing segregation, population distribution,
community crime, urban blight, etc., are recorded. The positive or
negative effects of these attributes to our urban quality of life
are specified, and the arena of happiness or satisfaction based on
individuals, community, and activities are interwoven with the
interdependent relationships among variables across the board.
The Quality of Life Model developed in the preceding chapter has been
completely expressed by its physical inputs, and the entire model
specification may look as follows:
QOLit = F (PHlt; PSit)
= F (ECit, P0lt, ENit, HElt, S0it I PSit)
and ECit = f (IEWBit,GEHit)
P0it = f (IAit,LGFit)
ENifc = f (IIEit,NEit)
HEit = f (ICit,CCit)
S0it = f (IDit,IEit,CLCit)
The model states that the QOL at the ith SMSA in time t may be
measured physically from the five goal components for a given level
of psychological inputs, or by holding constant the psychological
factors influencing the perceived level of quality of life among
SMSA's. The economic component is in turn measured by the concerns
with individual economic well-being (IEWB) and community economic
health (CEH); the political component by the concerns of individual
activities (IA) and local government factors (LGF); the environmental
component by the individual and institutional environment (HE) and
the natural environment (NE); the health and educational component
by the individual and community conditions (1C and CC) and finally,
the social component by individual development (ID), individual
equality (IE) and the community living conditions (CLC). These five
goal components are theoretically assumed to be independent. In
reality, however, their independent substance cannot be fully,
practically realized, and the representative variables selected
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for each goal component have to capture empirically some interdependent
relationships between events in this complex society to measure mean-
ingfully the level of quality of life among SMSA's.
Representative QOL indicators are delineated with data being collected
from both secondary sources as well as firsthand surveys. A detailed
chart listing all data sources according to the order of sequence of
variables appearing in this study is presented in the Appendix,
together with all data for the 243 SMSA's under discussion. Most of
the raw data have been transformed into forms with common units of
measurement. They can be valuable inputs to scientific verifications,
to other in-depth studies and extended research. Furthermore, it is
the first of its kind, i.e., a QOL statistics handbook with complete
coverage for all metropolitan areas in this country. A comparative
static analysis across the statistical tables can provide substantial
amounts of information for concurrent policy recommendations and
various decision making.
It should be noted that all variables measured by dollars were deflated
by the cost of living indexes prior to their employment and all estimated
data were marked with dots as shown in the Appendix.
INDICATOR CONSTRUCTION AND RATING SYSTEM DEVELOPMENT
The quality of life, as noted earlier, should be conceptually viewed
as a stock variable. Theoretically, it reflects the status of human
happiness and satisfaction at a particular point in time for the given
physical and psychological conditions with which the individual in
question is confronted. In Chapter III, a production model was
developed in order to measure the level of quality of life perceived
by any individual. In the model the level of quality of life is
operationally assumed to be the output produced by both psychological
and physical inputs. The output produced is generally referred to
as though it is. over a period of time and, hence, is a flow variable.
Conceptually, social indicators designed to reflect the quality of
life variations among metropolitan areas should be regarded as stock
variables and constructed on the basis that they reflect a specific
point in time. However, this presents an empirical problem since many
statistics available today are in the form of flow variables. Furthermore,
concerns with our social well-being have always been focused on issues
related to both flow and stock variables; public interests are not
likely to be dichotomized. As a result, the output production
approach was employed for operational purposes, and both physical and
psychological variables were selected as inputs to the model regardless
of their flow or stock characteristics.
79
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After the model has been specified and the variables included in the
model have been identified, clearly the next requirement in measuring
the variations in the level of quality of life among SMSA's is to collect
empirically the statistics and data needed to construct the QOL
indicators. Many technical problems arise relating to index construc-
tion and the development of the rating system. Generally, a model of
measurement should include several attributes not always embodied
in the model specification. Ideally, the index and weighting schemes
designed to measure the quality of life should possess the following
characteristics:
* They should distinguish between various levels of quality of life
for different persons at different locations and different points
in time.
* They should be embodied in an integrated model with their compila-
tion and use clearly related to public policy goals and interpre-
tations.
* They should be sufficiently universal that the underlying method-
ology is commonly understandable and generally acceptable for
collecting quantitative information.
* They should be scientific so that the techniques can be repeated
and verified.
* While they should be neutral and independent of variable units of
measurement, an increase in the numerical value of the indexes
should represent a better quality or a favorable trend.
The amount of effort that has been devoted to attaching quantitative
values to the quality of life indicators discussed above is
very limited, primarily because no consensus has emerged on what factors
are important and what appropriate weights should be assigned to the
important factors. In order to compare the measures associated with
the factors, a common approach is to obtain individual weightings
from the member of the sample population, i.e., through, an opinion
survey among the sample observations or the Delphi Procedure. This
is one specific approach used by Dalkey and others.—
4/ See N. C. Dalkey, Studies in the Quality of Life - Delphi and Decision
Making (Lexington, Massachusetts: D.C. Heath Company, 1972).
80
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It asks subjects to provide relative rankings of factors with
some systematic procedure such as "Splitting 100." It is, however,
very difficult in this approach to distinguish between the subjective
measures and relative weights.
The National Wildlife Federation's Environmental Quality Index was
constructed as the sum of the products of a subjectively rated
numerical scale of 0 to 100 (with 0 for a disaster and 100 for the
ideal condition) of the component measures (air, water, minerals,
soil, etc.), and the relative importance of the components in relation
to life (e.g., 30 points for soil, 20 for air and water, respectively,
etc). The index in 1971 was 55.5.^
In the survey of Hopes and Fears of the American People, Cantril
and Roll employed a 0 to 10 ladder-rating system on the "self-
anchoring striving scale" to measure the individual and national
accounts of hopes and fears by age, education, income, race and
political affiliation strata. A shift of 0.6 in a rating from past
to present and from present to future is considered statistically
significant. In the survey covering 3 years (1959, 1964, and 1971),
they found that Americans, on the personal level, express less
concern than they did 5 or 10 years ago with the material elements
that have traditionally comprised the "American Dream"; on the
national level, people gave this country a present rating almost one
step below that for the past, and a future rating that merely compen-
sates for the ground lost in the last 5 years. "The American people
clearly feel their nation is in trouble," noted Cantril and Roll.£'
The use of a matrix form for the quality of life measures followed
by derivation of the weighting scheme according to the perceived
Importance for each real measure in the matrix by the participants
has been another conventional technique.
Many attempts at developing social indicators without going through
a personal survey have simply weighted all the basic measures
equally in deriving an aggregate measure. This approach, while simple
and easily understood, has frequently been criticized on the basis
that many basic statistics are highly correlated; to weigh all these
measures equally in deriving a simple measure of quality of life could
be misleading. For this reason, Wilson and Smith have used factor
5_/ See for instance, National Wildlife Federation, "1971 National En-
vironmental Quality Index," National Wildlife (October-November
1971).
6/ A. H. Cantril and C. W. Roll, Jr., Hopes and Fears of the American
People (New York: Universe Books, 1971), p. 15.
81
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analysis to resolve the weighting problem. Factor analysis is one of
the techniques frequently used in multivariate studies. It not only
can reduce a large number of variables to a few components which
jointly explain most of the sum of the variances among the
variables but also can produce the loadings or weights for each
variable and, hence, the factor scores associated with each component.
Sample observations can then be rated or ranked .according to the factor
scores and the standardized original statistics.—/
The quality of numerical data available for the development of
national social well-being, such as the New Economic Welfare indicators,
leaves much to be desired, and the difficulties are apparently com-
pounded at the regional level. Given the present state of social
statistics, not only does the model specification have to be limited
to its selection with representative variables, but also frequently
the numerical series that have to be used are close to social indicators
defined in the model. In other words, the social indicators are
empirically measured by indirect surrogates, like death rate, and
physicians per capita rather than the exact years of life expectancy
and the true availability and accessibility to medical care. Another
particularly knotty problem encountered by index construction and
rating development is that of variable weights; we will comment on
this later.
Despite the nature of true indicators or indirect surrogates, three
kinds of regional social indicators have been recognized. According
to Kamrany and Christakis, there are absolute indicators, relative
indicators, and autonomous indicators.—' The absolute indicators are
those of scientifically established maximum or minimum levels for a
certain condition, such as the various pollution standards set by the
Environmental Protection Agency and the minimum wage rate enacted
by the U.S. Congress. The relative indicators are not bound by the
minimum or maximum levels, but rather measure the relative position
among regions, such as living cost and crime indexes, unemployment
and school attendance rates, etc. "With a common denominator, the
TJ See J. 0. Wilson, "Quality of Life in the U.S.--An Excursion into the
New Frontier of Social Economic Indicators," (Kansas City: Midwest
Research Institute, 1971), and D. M. Smith, The Geography of Social
Well-Being (New York: McGraw Hill, 1973).
J5/ For the three types of indicators, see N. M. Kamrany and A. N. Christakis
"Social Indicators in Perspective," Socioeconomic Planning
Sciences, k, (1970), pp. 207-216.
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relative indicators serve very well as comparative statistics for
interregional comparisions. The autonomous indicators are generally
referred to as conditions unique or specific to particular areas,
which are not common concerns over all regions. For instance, the
number of movie stars to total professional people and the number of
retired to working population may be very important social indicators
for Los Angeles and Phoenix, respectively; however, they are not
widespread social concerns.
In this study, both absolute and relative indicators were selected.
As shown in the preceding section, a careful choice has been made
between an absolute and a relative indicator when there are data which
offer both alternatives. Relative indicators are chosen in favor
of absolute indicators, mainly because this study is aimed at com-
paring the quality of life variations among SMSA's. Also for this
reason, no autonomous indicator was included in this study.—'
Three methods of indicator construction have been reviewed and considered
in this study: (1) the standardized additive method; (2) the adjusted
standardized additive method; and (3) the component and factor analyses.
Method 1; The standardized additive method involves the transformation
of data on individual variables into standard scores, which in turn are
added linearly to generate the quality of life indexes for each of the
five components. The conventional method of standardization is to use
the Z scores method. The Z score is a linear transformation of
the original data, such that the mean of the Z score becomes "0" and
its standard deviation becomes "1." In other words, two important
parameters of the initial distribution of the original data set are
normalized to show a uniform zero mean and unitary standard deviation.
The basic reason for this standardization is to eliminate the units
of measurement among different variables so that they can be neutral
and further operated with addition or subtraction, depending only on
the direction of those variables toward the explanation of the vari-
ations in the quality of life. For observation (i) on any variable
(j), the standardized score (Z^i) is measured by:
A decision on the appropriate goal or desired state is a prerequi-
site to determining the required numerical indicator. The abso-
lute indicators are of vital importance in judging the conditions
as to what constitutes a reasonable or minimum acceptable standard
for the QOL. A major effort in this area has been made by 0. W.
Markley and M. Bradley at Stanford Research Institute.
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A.* • *. •
z.. = JU _ i (i)
*-J o
sj
where Xj_4 is the original value that variable j takes for obser-
_ vat ion i ;
X* represents the mean value of all observations for the
variable j ; and
S* denotes the standard deviation of variable j
One of the most significant characteristics of this transformation is
that the Z scores are normally distributed with almost 99.8 percent
of transformed observations falling between values of (X^ + 3S}) or
"+3", 95.0 percent between (X. + 2Sj and 68.3 percent between
OT. + Sj) or "+2" or "+1", respectively, given that the original dis-
tribution is also normal .12'
Since all variables take values independent of the unit of measurement
after the transformation, the standardized additive method to obtain
the quality of life indexes for all SMSA's is simply to add or subtract
the weighted Z scores with weights being assigned to each of the
variables separately. To be more specific, the method of constructing
the QOL indicator "k" is given by
n n
Iik = ( Z WjZip/n — ^ Iik - ( E Zi:j)/n with Wj = 1.0 (2)
where Iik stands for the magnitude or the indexes value for the kth
component
W* is the weight assigned to variable j
n indicates the number of variables measuring the criterion in
question; or a subset of all variables used in the study.
If each variable in the subset is weighted equally, or with W. being
equal to unity, the indicator takes on the mean value of the individual
Z scores. In a like manner, the indexes for the five QOL components
are also treated as weighted averages of the indicator values, as follows:
10/ For discussion on normal distribution, see P. G. Hoel and R. J. Jessen,
Basic Statistics for Business and Economic (New York: John Wiley
and Sons, 1971).
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m
QiP = (k=l Wk i**)'* - ^
where Q. represents the quality of life index value for component
p for SMSA i * " *,.*,.-.— ,...-,...,
in the component.
p for SMSA i and m the number of indicators included
The three steps described above illustrate the standardized additive
method employed in this study with the weights being equal to unity
for all variables in the same category (or indicator) and for all
indicators in the same QOL component. The equal weighting scheme is
used for the sake of simplification because there is even less
theoretical guidance or consensus among social indicator researchers
with respect to weighting schedule than for the representative variable
selection. This lack of general agreement is entirely due to the
absence of a social preference function among members within the
society. The selection of generally agreed on variables in the social
welfare function is a difficult task for any researcher, but the
choice of a generally agreeable weighting scheme applicable to the
variables is even more formidable.
Although the attitudinal survey seems to be the only way of deriving
such weights theoretically, empirically it is not only costly but
also difficult to conduct. For instance, the attempt to introduce
the Dalkey and Rourke approach (described previously) to identify and
weigh the quality of life factors at the Conference on the Quality
of Life Concept sponsored by U.S. Environmental Protection Agency in
1972, was received with surprising hostility from a substantial
percentage of the attendees. Despite the substantial spread in the
weights that the conference attendees attached to the different
variables, the three major components of the QOL were given relatively
similar weights by them; on a "Splitting 100" scale, the economical
component received 31.8 points, environmental component 31.2 points,
and the political/social component 35.6 points.—' This leads one
to believe that the members tended to consider the major components
almost equally important.
There are five components in this metropolitan OOL study, i.e.,
economic, political, environmental, health and education, and social.
ll/ See U.S. Environmental Protection Agency, The Quality of Life
Concept (Washington, D.C.: The U.S. Government Printing Office,
1973), PRI - 78-80.
85
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Within each component, there are at least two category indicators--
generally one refers to individuals, another to the community. There
are also subcategories in these indicators, and many variables in each
subcategory. The equal weighting scheme employed in this study means
that variables in the same subcategory are weighted equally, and that
subcategory factors and component indicators at the same level are
weighted equally. Thus, the variables, factors, and indicators at
the same level among the five components are not necessarily weighted
equally; indeed, most of them carry different weights when intercom-
ponent comparisons are made.
For example, there are five variables in the wealth subcategory in the
economic component. The original values of these five variables were
first standardized or transformed to the Z scores as shown by equation
(1). The five Z scores were then weighted equally to derive the average
value for the wealth factor. According to equation (2), the wealth
and the standardized personal income per capita were weighted equally
to obtain the individual economic well-being indicator. In a similar
manner, the community economic health indicator was developed through
the standardized Z scores and the equal weighting process for the
variables such as the value added per work in manufacturing in the
productivity category, for the categories of economic diversification,
income inequality, unemployment rate, etc. Finally, the economic index
was derived by taking the average of these two indicators—an individual's
economic well-being and the community's economic health. As a result,
the variables in the wealth category were apparently weighted unequally
from those in the income inequality category as far as the construction
of the economic component index is concerned.
The equal weighting scheme applied to the variables at the same level--
subcategory, indicator category, and component--in this study has
another important aspect. Specifically, the weight attached to each
variable is determined implicitly after the model specification has
been completed as shown in the charts in the last section. For example,
the personal .income per capita variable has a weight five times as high
as the variable of median values of owner-occupied single family
housing units in the wealth category. The income and wealth variables
in the individual economic well-being indicator carry with each a
weight that is 2.5 times higher than those at the same level in the
community health economic indicator, such as the degree of economic
concentration and productivity. The community economic health indicator
has seven categories, while there are only two in the individual
economic well-being indicator. Therefore, the specification of the
level at which each variable is used in this study, as it appears in
86
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the five criteria charts, has been simultaneously assigned a variable
weight which, in essence, is based on the number of variables included
in each subcategory, the number of subcategories, and the number of
component indicators. This is the major reason for devoting a sub-
stantial amount of effort to a literature review and to the structure
development of the model.
Method 2; The adjusted standardized additive method differs slightly
from the standardized additive method in that the former approach, in
order to avoid extreme values, always converts the original standard-
ized data into grade points prior to the use of the aggregating and
weighting technique as aforementioned. Specifically, all observations
are divided into five grades based on the percentile distribution of the
Z scores. SMSA's received grade points ranging from "1" to "5" depend-
ing upon their respective Z scores according to the following schedule:
Z > 0.83 (= X + 0.83 S) 5 points
0.83 2 Z > 0.25 (= X + 0.25 S) 4 points
0.25 * Z > -0.25 (= X - 0.25 S) 3 points
-0.25 * Z > -0.83 (= X - 0.83 S) 2 points
-0.83 > Z - 1 point
In other words, every factor value for each SMSA has to be first con-
verted into an ordinal grade point according to its group standing
among the SMSA's in the same population size group. The SMSA's with
a Z score greater than 0.83 are given 5 points, while SMSA's with
a Z score less than -0.83 are given 1 point. The critical values
are chosen such that about 20.0 percent of the SMSA's are in the same
group should the Z scores be normally distributed. The basic justi-
fication for this adjustment is that the overall index construction
is based on the additive which, as generally desired, should be
neither significantly pulled up by the extreme high values of the
Z scores on certain variables nor substantially pushed down by the
extreme low values of the Z scores on certain other variables. In
terms of the purpose—evaluating the QOL among SMSA's--this adjustment
seems to be warranted and more desirable than omitting the adjustment.
After all Z scores have been replaced by the point scores, the similar
weighting scheme and the steps involved for QOL component indexes
construction noted earlier are taken to compute the adjusted standard-
ized scores for all observations.
87
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Although the standardized additive method still retains the character-
istic of having the zero mean value for all observations at the final
stage when the component QOL indexes have been developed, this special
mean value disappears in the adjusted standardized additive method.
As expected, these two methods of index construction will produce some-
what different rankings among SMSA's being evaluated. For purposes
of comparison, indexes derived from both methods will be reported for
each of the five QOL components in the following chapters of empirical
analyses. Nevertheless, more findings and results will be analyzed
with reference to the adjusted standardized scores than those that are
unadjusted.
The quality of life in the SMSA's is rated as Outstanding (A), Excel-
lent (B), Good (C), Adequate (D), and Substandard (E) in accordance
with their component indexes. The rating system used here is somewhat
arbitrary. It is assumed that SMSA's with an index value of one standard
deviation (S) beyond the mean level (X) should be rated Outstanding (A),
and SMSA's with an index value of one standard deviation below the mean
should be rated Substandard (E). The other three fall in between
(X" + S) and are rated, respectively, Excellent (X + 0.28 S £ B_< X + S),
Good (X - 0.28 S < C < "X + 0.28 S), and Adequate (X- S < D < X - 0.28 S).
If the distributions of the QOL component indexes are normal, this
rating system should give A's and E'S to the top and bottom 16.0 per-
cent of observations, respectively; and 23.0 percent would be in each
of the B's and D's; and 22.0 percent in the C's.
Method 3; The third method considered in this study is the factor
analysis. Factor analysis is a general name given to a class of
techniques whose purpose often consists of data reduction and summari-
zation. It does not entail partitioning the data into cause-effect
or dependent-independent subsets, nor does it provide any hypothetical
framework; rather, the analysis is primarily concerned with establish-
ing the "strength" of the overall relationships among the whole set of
variables selected in the study. In other words, this method attempts
to account for the maximum variation, or to best reproduce the observed
correlations in terms of a smaller set of linear combinations of the
original variables. The major substantive purpose of the factor
analysis is the search and test of structures or dimensions assumed to
underlie manifest variables. Frequently, its stress is more on data
reduction and description than hypothetical testing; and statistical
inference. However, it does provide one mathematical approach to
resolution of the weighting problem: no assumption with respect to
the weight of each variable is needed. For example, the standardized
88
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additive method had to assume that the five variables under the wealth
category in the economic component were weighted equally to derive
the score on wealth which, in turn, was weighted equally with the
personal income per capita input variable to compute the score for
individual economic well-being. Finally, the scores of the individual
economic well-being and the community economic health were averaged
to produce the QOL index for the economic component.
Two types of factor analyses have been widely applied to biological,
geographical, social, and economic studies: one is intended to develop
a smaller set of uncorrelated variables, which jointly can extract
the maximum variance from the original set of variables (these may be
highly intercorrelated), and the other is an attempt to best repro-
duce the observed linear correlations in the original set of variables.
The former is conventionally referred to as the principal component
analysis, while the latter is usually called the factor analysis.
The mathematical operation for extracting the maximum variance from
the original n variables (X^,...Xn) is shown as follows:
zl = A11F1 + A12F2 + ••+AlnF
Zn = A,FT + A 0F, + ..+A F
n nl 1 n2 2 nn n
where Z's are the standardized form (with zero mean and unit standard
deviation) of the observed variables, and are expressed as a linear
combination of n new components F]_, F£ • . . Fn which are uncorrelated
among themselves but each of them, in order of importance, makes a
maximum contribution to the sum of the variances of the original n
variables. The A's are factor weights or the correlation coefficients
between the original variables and the new factor component. The sum
of the squared A's for any factor over all variables observed is
called the eigenvalue (X) for that factor. For component factor k,
the eigenvalue (X^) is also equal to the maximum amount of variance
among the original variables accounted by the factor, V^, i.e.,
n 2
*k " vk " i A k
89
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Once the factor loadings or weights for each variable are determined,
a set of indicator or factor scores (1^) associated with each component
factor k can be derived from the set of the standardized, initial
statistics Z j . To be specific,
n
• z
In practice, a great portion of the total variance among the original
set of variables can be explained by a few members or components. As
a result, the component analysis provides an efficient summarization
of the data.
The mathematical expression of the factor analysis which seeks to best
reproduce the observed correlation among the original variables is
slightly different from the component: the n original variables are
expressed as a linear function of m (m < n) common factors (F) and
one unique factor (U)--
Zl = bllFl + b!2F2 + •••+blmFm + elul
Zn - bnlFl + bn2F2 + •••+bnmFm + enun
The common factors account for the correlations among the variables
while the unique factor is used to account for the remaining variance
on the residual of that variable. The factor scores for the factor
analyses cannot be exactly determined as described above for the
component analysis. The conventional least-squares regression
technique has to be employed to estimate the factor scores in the
factor analysis, and the b's and e's are factor loadings or weights
from the regression study.
Both component and factor analyses can begin with a simple correlation
matrix of dimensions (n x n) for a set of n original variables taking
on standardized Z values. The solutions of a principal component
analysis require the correlation matrix with values of unity in
the principal diagonal and then performing an orthogonal transformation,
transforming the n original variables into a new set of n components.
The factor analysis allows less than unity values for the principal
diagonal elements in the correlation matrix, or requires only the
estimated values of communalities in the diagonal. The number of
factors constructed as best uncorrelated representations of the original
variables is less than that of the original variables because there is
a unique variable in the model. Given a nonsingular matrix to begin
90
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with, the factor scores for the component analysis can be determined
exactly as noted earlier and are unique. Nevertheless, the factor
analysis involves both common and unique factors with the total number of
factors exceeding the original number of variables. Thus, an inverse
does not exist for such a singular correlation matrix, and the
general approach, to estimate the factor scores is to regress factor
(Fk) on the n variables. Further discussions of, and applications to,
factor and component analyses can be found in Addman and Morris,
Crew, Guertin and Bailey, and Harman.I±/
The application of the principal component method by bringing all
variables up to the same level and pulling them together for statistical
operation, however, violates our theoretical concept of quality of
life input framework--such a procedure ruins the hierarchical
structure based on the hypothesized importance of each variable
towards explaining the total variations in the quality of life among
regions. Many studies measuring the quality of life in the U.S.
found little difference between ranking produced by the standardized
additive methods, and by the complicated method of factor and component
analyses.—' For these reasons the results from the principal
ll./ See Irma Addman and Cynthia T. Morris, "A Factor Analysis of the
Interrelationship Between Social and Political Variables and Per
Capita Gross National Product," Quarterly Journal of Economics
(November 1965), pp. 555-578; Robert E. Crew, Jr., "Dimensions
of Public Policy: A Factor Analysis of State Expenditures,"
Social Science Quarterly (September 1969), pp. 381-389; W. H.
Guertin and J. P. Bailey, Introduction to Modern Factor Analysis
(Ann Arbor, Michigan: Edwards Brothers, Inc., 1970); and
H. H. Harman, Modern Factor Analysis (Chicago: Chicago University
Press, 1966).
.12/ In the quality of life study by John Wilson, state ranks computed
from both factor analysis, using squared multiple correlation
coefficients as estimates of existing communalities, and the
principal component analysis were compared and showed a very
highly significant spearman rank order correlation coefficient of
about 0.96. In the interstate geography of social well-being,
Smith found that the rank correlation coefficient between the
general social well-being indicator derived from summing the
unweighted Z scores and the indicators from the component
analysis is 0.914. In other words, little difference is ob-
served in state rankings so far as different weighting methods are
concerned. See John Wilson, "Quality of Life in the U.S."
(Kansas City: Midwest Research Institute, 1970), p. 22; and
David Smith, The Geography of Social Well-Being (New York:
McGraw-Hill, 1973), p. 101.
91
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component analysis will not be completely presented for all QOL
components in the following chapters. Nevertheless, the quality of
life rankings for the economic component computed by this method will
be employed and analyzed strictly for the purpose of methodological com-
parison.
In the following three chapters empirical findings on QOL variations
and their policy implications will be discussed respectively for the
large, medium, and small group of SMSA's. Again, only intragroup
variation comparisons are legitimate. Intergroup comparisons are
prohibited because the project is designed to measure the QOL variations
among SMSA's within the same population size group. The original
statistics are respectively normalized with their own group mean and
standard deviation. Thus, SMSA's rated outstanding in one group may
possibly be rated only excellent or good if they were in other groups,
and vice versa.
92
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CHAPTER V
QUALITY OF LIFE FINDINGS AND IMPLICATIONS:
LARGE METROPOLITAN AREAS (L)
In 1970, there were 65 SMSA's in this country with a population of
more than 500,000 persons. Geographically, most of these SMSA's are
located in the Middle Atlantic and the East North Central regions of
the U.S. There are no large SMSA's in the States of Alaska, the Dakotas,
Delaware, Idaho, Maine, Mississippi, Montana, Nevada, New Hampshire,
New Mexico, South Carolina, Vermont, West Virginia, and Wyoming. As a
result, the quality of life comparisons for the large SMSA's (L) mainly
refer to the most densely populated states in the U.S., especially in
the East. (See Figure 1.)
According to the model development, the five components of the quality
of life measures, findings, and implications will be discussed in the
following order: economic, political, environmental, health and edu-
cation, and social. A brief summary will be given in the last section.
ECONOMIC COMPONENT
The economic component constitutes one of the basic physical inputs to
our quality of life. Material wealth satisfies our fundamental need
for survival, or meets the minimum requirement of freedom from hunger.
A decent standard of living was a most important concern, second only
to personal health, among all Americans surveyed by Cantril and Rolls
for the periods from 1959 to 1971.i' A broad concept of personal
command over goods and services—defined as the ability of individuals
and families to obtain and consume those goods and services available
through both the public and private sectors—has been used as the basis
for selecting the relevant variables for the study.
I/ See Hadley Cantril, The Pattern of Human Concerns (New Brunswick,
New Jersey: Rutgers University Press, 1965), p. 35; and A. H.
Cantril and C, W. Rolls, Jr., "Hopes and Fears of the American
People" in Environmental Protection Agency, The Quality of Life
Concept (Washington, D.C.: Governmental Printing Office, 1973),
p. 69.
93
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Table 1 contains indexes and ratings of the economic component of all
65 large SMSA's.. As of 1970, in terms of economic strength, the Dallas,
Texas, SMSA had the highest adjusted standardized score among the large
SMSA's, given the structure organization of the economic variables
proposed in this study. The index value for Dallas is 2.76, or about
1.9 standard deviations above the mean value (1.74) for all 65 SMSA's.
The Houston SMSA, with an index slightly below that of Dallas (2.70),
ranked second; and Portland, Oregon/Washington SMSA with an index
insignificantly different from Houston (2.68), ranked third. Cleveland,
Ohio; Indianapolis, Indiana; Fort Worth, Texas; Atlanta, Georgia;
Chicago, Illinois; Cincinnati, Ohio/Kentucky/Indiana; and Richmond,
Virginia, completed the top 10. The remaining two areas with index
values above the mean plus one standard deviation (0.55) are still rated
"A" or categorized as "outstanding"; they are Rochester, New York
Fort Lauderdale, Florida, and Hollywood, Florida. They are marked with
stars in Figure 1.
There are 16 SMSA's with an index valued between 1.89 (x + 0.28 S)
and 2.29 (x + S). They are rated "B" or excellent. Most industrialized
and manufacturing-oriented SMSA's, such as Seattle/Everett, Los Angeles,
Minneapolis/St. Paul, St. Louis, Grand Rapids, Detroit, Dayton, New York,
and others are in this group. They are marked with dots in Figure 1.
The outstanding (A) and excellent (B) SMSA's are distinguished from
the others by a. combination of factors. They are outstanding or
excellent not only in the sense of individual economic well-being,
represented by personal income and wealth, but also have a very healthy
regional economy with higher labor productivity and lower unemployment
rate, more diversified economic structure and equal distribution of
income, a larger pool of available capital funds, and a greater local
effort in stimulating regional economic growth. In other words,
measures in the economic component are related to the individuals as
well as the community in which individuals conduct their economic life.
These measures cover the three vital functions of the economic per-
formance- -product ion, distribution, and consumption.
In contrast, 13 SMSA's are rated "E" or substandard because of their
low index values—lower than the mean minus one standard deviation
(or 1.19). Jersey City, New Jersey, which received an adjusted
standardized score of 0.59, ranked last on the list. Reading from
Jersey City upwards are: San Antonio, Texas; New Orleans, Louisiana;
Norfolk/Portsmouth, Virginia; Jacksonville, Florida; Memphis, Tennessee/
Arkansas, Philadelphia, Pennsylvania/New Jersey, Birmingham, Alabama,
etc.
94
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TABLE 1
INDEX AND RATING OF ECONOMIC COMPONENT (L)
Adj\nt«d Standardized Scores
Standardlled Scores
1.
2.
3.
4.
5.
6.
7.
6.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34,
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
SMSA
Akron, Ohio
Albany-Schenectady-Troy, N.Y.
Allentown-Bethleheo-Easton, Pa.-N.J.
Anaheim-Santa Ana-Garden Grove, Ca.
Atlanta, Ca.
Baltimore, Md.
Birmingham, Ala.
Boston, Mass.
Buffalo, N.Y.
Chicago, 111.
Cincinnati, Ohlo-Ky.-Ind.
Cleveland, Ohio
Columbus, Ohio
Dallas, Texas
Dayton, Ohio
Denver, Colo.
Detroit, Mich.
Fort Lauderdale-Hollywood, Fla.
Fort Worth, Texas
Gary-Hammond-East Chicago, Ind.
Grand Rapids, Mich.
Greensboro-Wlnston-Salem-Hlgh Point,
N.C.
Hartford, Conn.
Honolulu, Hawaii
Houston, Texas
Indianapolis, Ind.
Jacksonville, Fla.
Jeraey City, N.J.
Kansas City, Mo.-Ks.
Los Angeles-Long Beach, Ca.
Louisville, Ky.-Ind.
Memphis, Tenn.-Ark.
Miami, Fla.
Milwaukee, Uls.
Mlnneaoolls-St. Paul, Minn.
Nashville-Davidson, Tenn.
New Orleans, La.
New York, N.Y.
Newark, N.J.
Norfolk- Portsmouth, Va.
Oklahoma City, Okla.
Omaha, Nebraska- Iowa
Paterson-Cllfton-Passalc, N.J.
Philadelphia, Pa.-N.J.
Phoenix, Ariz.
Plttsburg, Pa.
Portland, Oreg.-Waah.
Providence-Pawtucket-Warvick. R.I.-
Mass.
Richmond, Va.
Rochester, N.Y.
Sacramento, Ca.
St. Louis, Mo. -111.
Salt Lake City, Utah
San Antonio, Texas
San Bernadino-Rlverside-Ontarlo, Ca.
San Diego, Ca.
San Francisco-Oakland, Ca.
San Jose, Ca.
Seattle-Everett, Wa.
Springfleld-Chlcopee-Holyoke,
Mass. -Conn.
Value
1.8786
1.3286
1.4286
2.1786
2.4714
1.3429
1.0500
1.1786
1.8357
2.3643
2.3429
2.5143
1.7857
2.7571
2.1214
1.8357
1.8929
2.3143
2.4786
1.3929
2.2643
1.1571
2.0357
1.1357
2.7000
2.5143
0.8929
0.5857
1.6857
2.0500
1.9071
0.9429
1.2857
2.1786
1.9357
1.7286
0.7857
1.9500
1.2571
0.8500
2.1143
2.2786
1.9357
0.9500
1.2786
1.5929
2.6786
1.0786
2.3357
2.3214
1.5929
2.0357
1.3714
0.7857
1.2000
1.8786
1.8357
1.7500
2.1071
1.1357
Rank
29
47
43
15
7
46
58
53
32
8
9
4
35
1
18
33
28
12
6
44
14
54
22
55
2
5
61
65
38
21
27
60
48
16
25
37
63
24
50
62
19
13
26
59
49
41
3
57
10
11
40
23
45
64
52
30
34
36
20
56
RattnR
C
D
D
B
A
D
E
E
C
A
A
A
C
A
B
C
B
A
A
D
B
E
B
E
A
A
E
E
C
B
B
E
D
B
B
C
E
B
D
E
B
B
B
E
D
C
A
E
A
A
C
B
D
E
D
C
C
C
B
C
Value
0.0713
-0.0939
-0.1180
0.4038
0.5041
-0.2146
-0.6756
-0.1819
0.0405
0.2824
0.3522
0.3409
-0.0127
0.7489
0.2159
0.1216
0.1044
0.6708
0.4829
-0.1564
0.3755
-0.2434
0.3958
-0.4047
0.5379
0.3946
-0.5800
-1.1323
0.0158
0.3507
0.1031
-0.5872
-0.1016
0.2858
0.0886
0.0025
-0.7046
0.3003
-0.3293
-0.6368
0.1935
0.2688
0.0597
-0.5513
-0.1706
-0.0636
0.8879
-0.3613
0.3264
0. 3205
-0.2183
0.1120
-0.2660
-1.0204
-0.4286
0.1471
0.0565
0.0814
-0.0328
-0.4301
Rank
31
42
44
7
5
48
62
47
35
18
11
13
38
2
21
24
27
3
6
45
10
50
8
55
4
9
59
65
36
12
28
60
43
17
29
37
63
16
53
61
22
19
33
58
46
41
1
54
14
15
49
26
51
64
56
23
34
30
39
57
Rating
C
C
D
A
A
D
E
D
C
B
B
B
C
A
B
B
C
A
A
D
B
D
B
E
A
B
E
E
C
»
C
E
C
B
C
C
E
B
D
E
B
B
C
E
D
C
A
D
B
B
D
B
D
E
E
B
C
c
£
P
61. Syracuse, K.I.
62. Tampa-St. Petersburg, Fla.
63. Toledo, Ohio-Mich.
64. Washington, D.C.-Md.-Va.
65. Youngs town-Warren, Ohio
A - Outstanding (i x + s)
B - Excellent (x + .28s i B < x + s)
C - Good (S - .28s < C < S -f .28s)
B " Adequate (»- s < D s x - .28a)
E - Substandard (•: S - s)
1.2071
1.6214
2.1714
1.8571
1.5857
Mean (
Standard
51
39
17
31
42
x) - 1.7390
Devlatlon(s)
D
C
B
C
D
- .5475
-0.2962
0.0705
0.2362
0.1154
-0.0540
Mean
Standard
52
32
20
25
40
(x) - 0.0000
Devlatlon(s) - (
n
C
B
B
C
).39
95
-------
c
0)
c
o
a
o
o
•H
O
C!
O
O
w
en
60
•H
4-1
C3
C
o
•l-l
4-1
3
CO
•r-l
O
60
O
-------
As expected, the findings in this study differ from those which employ
only one or several arbitrarily selected factors as economic measures,
such as the studies by Louis and Flax. A vivid example is that in Louis'
study, in the affluence component Honolulu was rated as one of the
finest cities by the measures of median income per capita and the
percentage of families below the poverty income level. However,
in this study, Honolulu with an index value of 1.14 is rated "E"
substandard.
One of the reasons for this significant difference is, as correctly
pointed out by Louis himself, that the Census Bureau statistics on
individual and family income may be somewhat misleading since they
are not adjusted for differences in the cost of living.— In this
study the personal income variable and, in fact, all other variables
with dollars as units of measurement, were deflated by the cost of
living index before the other indexes were developed so that they
become "relative indicators"--relative in terms of real purchasing
power. Although the nominal income per capita in the Honolulu SMSA
in 1969 was extremely high, $3,484, or about 11.0 percent higher than
the national average of $3,139 (see Table A-l in the Appendix), the
cost of living index for the SMSA was even higher, 124.6 versus 100.0
(see. Table A-5 in the Appendix). Consequently, the adjusted personal
income per capita deflated by the cost of living was equivalent to
$2,796 or only 89.1 percent of the U.S. average. Therefore, based
on per capita income, the Honolulu SMSA is not rated high in this
study.—' Furthermore, income and the percentage of families with
income below the poverty level are only two of 18 factors selected in
this study. These two factors alone cannot reflect the overall
affluence of the region because the stock of wealth and the viability
of economic structure are not taken into account. In addition, the
distribution of income would also have an effect upon regional quality
of life. Considering all these factors jointly, the Honolulu SMSA
was evaluated slightly below "adequate." Once again, readers should
be alert that the ratings in this study are "relative" and not
absolute terms. For example, Honolulu is relatively substandard only to
the other 64 large SMSA's being studied.
21 See Arthur Louis, "The Worst American City - A Scientific Study to
Confirm or Deny Your Prejudices," Harper's Magazine (January 1975),
pp. 67-71.
3/ For the same reason, Washington, D.C., SMSA and Paterson/Clifton/
Passaic SMSA are ranked, respectively, 12th and 20th in adjusted
personal income among the 65 SMSA's in this study rather than the
first and second highest as shown by their unadjusted incomes.
97
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Another example of contrast is the Dallas and Houston SMSA's. Flax
observed that both Dallas and Houston SMSA's, among the 18 largest
SMSA's in this country, were ranked, respectively, 7th and llth in
income and 16th and 17th in poverty.^' These SMSA's are rated the best
two in the economic component of our study of the 65 large SMSA's for
these reasons: Dallas had very high rankings in productivity,
available capital funds, and had a low unemployment rate; Houston had
very high rankings in economic diversification and percentage of labor
force employed. These favorable factors in balance made the two
SMSA's outstanding.
Figure 1 provides information on geographical distribution of the 65
large SMSA's. A quick review of the map suggests that most of the
SMSA's in the East North Central region had outstanding or excellent
economic quality of life while the substandard ones (marked by squares)
are found in the Middle Atlantic and in the South. All large SMSA's west
of the Missouri River, except Honolulu, Hawaii and San Antonio, Texas,
rated better than substandard in terms of the economic component. The
picture revealed in this study for 1970 is similar to the concentration
pattern of the so-called "industrial belt," and even more so to other
factors in the 1950's, as presented by Ullman, such as the distribution
of patents issued--a measure of innovation; of headquarters of the
largest industries—a measure of decision making; and. of Class One
railroads in the U.S.--a measure of efficient transportation.—
The outstanding and the substandard SMSA's can exist concomitantly not
only within one state, but also in a neighboring area: notable examples
are Dallas, Houston, and Fort Worth versus San Antonio in Texas; and
Richmond versus Norfolk/Portsmouth in Virginia.
In the light of regional economic growth theory which postulates
"spread" and "backwash" effects, these are interesting observations.
The spread effect refers to favorable impact of growth in the thriving
center: the region around a center tends to gain from increasing demand
by the center for agricultural products and raw materials and may feel
the benefits of technical spillover. The East North Central region
probably demonstrates the spread effect of economic growth. The
backwash effect, as argued by Myrdal, implies that the beneficial effects
of the growth center may be outweighed by the adverse effects: i.e.,
4/ See M. J. Flax, A Study in Comparative Urban Indicators: Condition
in 18 Large Metropolitan Areas (Washington, B.C., Urban
Institute, 1973).
5_/ See Edward L. Ullman, "Regional Development and the Geography of
Concentration," Papers and Proceeding of the Regional Science
Association, Volume 4, (1958).
98
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movements of labor, capital goods, and services generally favor the
prosperous center at the expense of the poorer neighboring regions.—
For example, migration may have harmful repercussions on the age
distribution of the population in the originating region,and the capital
market will deflect savings from poor regions where the effective demand
for capital is low to the growing regions where returns on capital are
high and less risky, etc. The cases in Texas (San Antonio) and
Virginia (Norfolk/Portsmouth) may be attributed to the backwash effect.
To the decision makers the implication of this drastic contrast due
to the backwash effect is whether or not in the future any state should
consider a balanced growth policy or a concentrated growth policy.
If balanced growth among regions is preferred, then various policies
should be directed at examining the problems and seeking the means to
improve the economic strength in the lagging regions. For instance,
San Antonio and Norfolk/Portsmouth showed, respectively, an index of
0.79 and 0.85 in the economic component, and both are rated economically
substandard. However, their individual problems are substantially
different and thus require different corrective policies. Based on the
static analysis on which this study is designed, it is appropriate to
point out that what is needed by people in San Antonio is the know-how
to enhance their productivity and economic diversity so that the income
flow can be enlarged. These factors are relatively worse than others
in the economic component. For Norfolk/Portsmouth, however, the flow
of income in 1970, on a per capita basis,did not seem to be as serious
a problem as the stock factors of wealth, or as the shortage of local
capital funds measured by bank deposits per capita. While unemployment
did not present a special problem in Norfolk/Portsmouth, there were a
relatively significant large number of families with income below the
poverty level--13.4 percent or 25 percent higher than the U.S. average
(see Table A-l in the Appendix). This implies either too many non-
working dependents in each family or a large income gap among families or
both prevailed in the SMSA. In a similar manner, diagnoses can be
performed for all SMSA's rated substandard in the hope that their
economic conditions will eventually be bettered.
6/ For these two countervailing sets of forces and arguments, see
J. T. Romans, Capital Exports and Growth Among U.S. Regions
(Middletown, Connecticut: Wesleyan University, 1965); G. H. Borts
and J. L. Stein, Economic Growth in a Free Market (New York:
Columbia University Press, 1964); and G. Myrdal, Economic Theory
and Underdeveloped Regions (London: Duckworth, 1957).
99
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Although all 12 SMSA's marked with stars are rated outstanding, the
economic weakness and strength among them can also vary substantially.
For instance, Fort Lauderdale/Hollywood SMSA ranked first in wealth as
a result of having the highest property to personal income ratio
(0.26 against 0.14 with U.S.), an extremely high percentage of owner-
occupied housing units (72.8 percent versus 62.9 percent in the U.S.)»
and more than nine out of 10 households with one or more automobiles.
In spite of relatively low productivity among workers in the area, the
unemployment rate was only 3.4 percent in 1970, or 1 percentage point
below the U.S. average. In addition, this SMSA is one of several
regions with high equality in income distribution between the central
city, the suburbs, and among all families. Chicago, on the contrary,
was one of the regions with the highest adjusted personal income per
capita but ranked only 12th in Individual Economic Well-Being because
of a relatively low wealth level — especially in terms of housing and
automobile ownership. Even though there was a very unequal distribution
of income between city and suburban families (ranked 59th) and little
effort to stimulate the local economy,.Chicago benefited substantially
from readily available capital funds, high employment., and productivity.
On the whole, Chicago was rated outstanding and ranked eighth among the
65 SMSA's under consideration. It has been shown that any outstanding
SMSA just as the substandard ones, may have weak spots in the economic
component. This study provides useful information for detecting the
total economic condition for each of the SMSA's.
In our earlier quality of life state study, the State of Georgia
received a very low index for its economic status (0.67 or 67.0 percent
of the U.S. average), and rated as substandard. Also, a number of
other quality of life studies concur with our findings that the overall
quality in Georgia rated lower than 40th among the 50 states.—
When interest is really in regional comparison, evaluations on the basis
of the state average are not very meaningful, if not misleading. Although
this is the reason for initiating a regional study, this study does
generate promising results. The Atlanta SMSA in Georgia, for example,
ranks outstanding in the economic component among the 65 large SMSA's.
Neither the States of Texas nor of Florida showed better than the U. S.
average economic status in the earlier study for states, but this study
Tj For comparisons see Ben-chieh Liu, The Quality of Life in the
United States 1970 (Kansas City Midwest Research Institute, 1973),
pp. 14 and 23; and "Quality of Life: Concept, Measured Results,"
The American Journal of Economics and Sociology (January 1975) ,
pp. 1-13.
100
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reveals that one-third of the SMSA's rated outstanding in the economic
component are in Texas and Florida. These comparisons indicate the
importance of a regional study and the preferability of the SMSA study
over the state study.
The variation among the SMSA's in economic conditions can be measured
by the "coefficient of variation," which is the ratio of the standard
deviation divided by the mean. The higher the value, the greater the
variation.— The coefficient of variation for the 65 SMSA's is 0.32
(0.5475/1.7390). As noted in Chart 1, there are 25 SMSA's with
adjusted standardized scores outside the range of mean plus and minus
one standard deviation (X + S), and the best and the worst SMSA differ
in index value by as much as four standard deviations. The variation
is smaller between scores for those SMSA's rated "good" than for those
rated "adequate." Chart 1 is organized according to the order of ranks
on the basis of the adjusted standardized scores contained in Table 1.
As noted in the preceding chapter, four methods of index construction
were developed. The results from the standardized "Z" scores method
differ only slightly from those adjusted standardized scores as ex-
pected--the rank order correlation coefficient between the two sets
is highly significant and is equal to 0.96. However, the weighted index
computed from the component analysis with the first three principal
components which jointly explained more than 50 percent of the total
variance, and those obtained from the factor analysis with the weights
from the first four major factor scores produced considerably different
rankings, especially for SMSA's rated "B," "C," and "D" by the other
two methods. Consequently, the rank order correlation coefficients (r)
between the results derived from the standard score methods and the
component and factor analyses are very low: between the adjusted
standardized scores and those of the principal component and the factor
analysis, r = 0.14 and r = 0.38, respectively; between the standardized
scores and those of the principal component and factor analyses,
r = 0.19 and-r = 0.33, respectively. Since a detailed technical
investigation on factor or component analysis is beyond the scope of
this work and the rankings are inconsistent, the empirical results
from factor and component analysis will not be reported and discussed
throughout the following chapters.
_8/ For statistical presentation, reference to the coefficient can be
found in most elementary statistics books. See A. Haber and
R.P. Runyon, General Statistics (Reading, Massachusetts: Addison-
Wesley Company, 1969), pp. 102-104.
101
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CHART 1
REGIONAL VARIATIONS IN INDEXES; ECONOMIC COMPONENT (L)
RANK
SMSA
1 Dallas. Texas
2 Houston, Texas
3 Portland, Oreg - Wash
4 Cleveland, Ohio
5 Indianapolis, Ind
6 Fort Worth. Texas
7 Atlanta, Go
8 Chicago, III
9 Cincinnati. Ohio - Ky - Ind
10 Richmond. Va
11 Rochester. NY
12 Fort Louderdole - Hollywood. Flo
13 Omaha, Nebr - Iowa
14 Grand Rapids, Mich
15 Anaheim- Santa Ana - Garden Grove, Calif
16 Milwaukee, Wis
17 Toledo, Ohio- Mich
18 Dayton, Ohio
19 Oklahoma City. Okla
20 Seattle - Everett, Wash
Q 21 Los Angeles- Long Beach, Calif
22 Hartford. Conn
23 St.Louis, Mo-III
24 New York, NY
25 Minneapolis- St.Paul, Minn
26 Paterson - Clifton - Passaic. NJ
27 Louisville, Ky - Ind
. 28 Detroit, Mich
29 Akron. Ohio
30 San Diego, Calif
31 Washington, DC - Md - Va
32 Buffalo, NY
33 Denver, Colo
34 San Francisco - Oakland, Calif
35 Columbus. Ohio
36 San Jose, Calif
37 Nashville- Davidson, Tenn
38 Kansas City, Mo - Ks
39 Tampa - St. Petersburg, Flo
40 Sacramento, Calif
. 41 Pittsburgh. Pa
' 42 Youngstown - Warren, Ohio
43 Allentown - Bethlehem - Easton, Pa - NJ
44 Gary - Hammond - East Chicago. Ind
45 Salt Lake City, Utah
46 Baltimore. Md
J.) < 47 Albany - Schenectody - Troy, NY
48 Miami, Flo
49 Phoenix. Aril
50 Newark. NJ
51 Syracuse. NY
52 San Bernodino - Riverside - Ontario, Calif
53 Boston, Mass
54 Greensboro - Winston - Salem - High Point, NC
55 Honolulu, Hawaii
56 Springfield - Chicopee - Holyoke, Moss - Conn
57 Providence - Powtucket - Warwick, Rl - Moss
58 Birmingham, Ala
E S 59 Philadelphia. Pa - NJ
60 Memphis, Tenn - Ark
61 Jacksonville. Fla
62 Norfolk- Portsmouth, Va
63 New Orleans, La
64 San Antonio, Texas
65 Jersey City. NJ
ADJUSTED STANDAIilZED SCORE
X-5
X+S
X-S
X*Meon = 1.7390
S « Standard Deviation = .5475
102
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POLITICAL COMPONENT
In evaluating the metropolitan quality of life the primary political
concerns may be differentiated according to those in which the individuals
participate directly and those that affect the individuals collectively.
In other words, political concerns may be evaluated through both
individual and institutional factors. In this study, the criteria are
centered on how well people are informed and involved, how efficiently
the local governments perform, how qualified the employees in the public
sector are, and how much welfare assistance is provided for the needy.
Specifically, this section is concerned with the factors of input to the
political arena and output of public goods and services produced by
the local governments. Metropolitan areas with better informed and
more involved citizenry, higher quality of public administration, and
greater collaboration and shared power among all levels of government
would be ranked above the others that lack such elements.
While the mass communication channels or the news media are used to
reflect the degree to which private citizens are informed, due to
lack of data, only one indicator was selected for political activity
participation or individual involvement—the ratio of presidential
votes cast to voting age population. The professionalism of the local
governments can be evaluated both on the qualification of public
employees—a quality consideration,and the amount of public service
performed by the public employees--a quantity consideration. The
entrance or average salaries of teachers, policemen, and firemen
are conventional indicators of their qualification. Therefore, four
salary variables were included in this study. As explained earlier,
throughout this study any variable measured by dollars and cents was
first deflated by the cost of living index to give a real term in the
sense of purchasing power. Thus, the nominal values were deflated prior
to index development. If the productivity of public employees does
not vary among regions, the services produced among regions may vary
because of the different numbers of people employed. For this reason,
the number of public employees per 1,000 population was chosen as a
quantity criteria.
Safety and security are basic daily concerns, and the performance of
local governments is often judged by crime rates. Violent crimes and
property crimes are substantially different in nature. Hence, both
factors were chosen as criteria. Community health and local educational
environment are equally important, but probably less sensitive criteria
than the crime rates. These considerations, plus the power shared with
other levels of government in raising revenues, jointly determine the
performance of the local governments. From the human welfare and the
equal rights points of view, the public is responsible for assisting the
103
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handicapped and the needy. Therefore, the following rating and ranks
among the metropolitan areas were derived from the more than 20 factors
just mentioned.
Among the indexes and ratings shown in Table 2, the outstanding SMSA's
in the political category are Buffalo, Albany/Schenectady/Troy,
Rochester, and Syracuse in New York, Grand Rapids, Michigan; Hartford,
Connecticut; Sacramento, California; Portland, Oregon/Washington;
Minneapolis/St. Paul, Minnesota; Boston, Massachusetts; Salt Lake
City, Utah; and Milwaukee* Wisconsin. Immediately after Milwaukee in
Chart 2 are the 15 excellent SMSA's, starting with Detroit and
Philadelphia and ending with Cincinnati and Oklahoma City. There are
also 15 SMSA's with "E" ratings, referred to as "substandard"—a
relative term meaningful only when they are compared to the other 50
large SMSA's in this country. In contrast to the four outstanding
SMSA's in New York, all four SMSA's in Texas fall in this substandard
category, with San Antonio at the bottom.
While Buffalo was disclosed to have an index as high as 3.88 for the
political quality of life, the corresponding figure for San Antonio is
only 1.34. Given the mean index value of 2.62 for all 65 SMSA's, these
two indexes are, respectively, 48 percent above and 48 percent below the
mean. Buffalo is shown to be one of the three best regions in pro-
viding public welfare assistance to the needy people in real terms
rather than nominal dollar amount. The people in Buffalo may be
considered best informed since it is one of the three SMSA's with the
highest ratio of local radio stations and Sunday newspapers in circu-
lation to population, and of television sets to occupied houses.
According to adjusted salaries of teachers, policemen, and firemen,
and the number of public employees per 1,000 population,, Buffalo ranked
high in local government professionalism. People in San Antonio, on
the contrary, received a very small amount of real public welfare
assistance, and the public employees in the area were paid low salaries
that when deflated by the cost of living index were slightly higher
than the U.S. average at 100.9. (See Table A-5 in the Appendix.) In
fact, the average monthly earnings of teachers in San Antonio were
$559 in 1970, the lowest among the 65 SMSA's without the cost of living
adjustment, or equal to 82.0 percent of the U.S. average of $682.
(See Table A-2 in the Appendix.) The professionalism of local govern-
ments in this area compared least favorably to its counterparts.
104
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TABLE 2
INDEX AND RATING OF POLITICAL COMPONENT (L)
SMS*
1. Akron, Ohio
2. Albany-Schenectady-Troy, N.Y.
3. Allentown-Bethlehem-Eaaton, Pa.-N.J.
4. Anaheim-Santa Ana-Garden Grove, Ca.
5. Atlanta, Ga.
6. Baltimore, Md.
7. Birmingham, Ala.
8. Boaton, Masa.
9. Buffalo, N.Y.
10. Chicago. 111.
It. Cincinnati,Ohio-Ky.-Ind.
12. Cleveland, Ohio
13. Columbus, Ohio
14. Dallas, Texas
15. Dayton, Ohio
16. Denver, Colo.
17. Detroit, Mich.
18. Fort Lauderdale-Hollywood, Fla.
19. Fort Worth, Texas
20. Gary-''aimnont -East Chicap.o, Ind.
21. Grand Rapids, Mich.
22. Greensboro-Wlnston-5alem-High Point,
N.C.
23. Hartford, Conn.
24. Honolulu, Hawaii
25. Houston, Texas
26. Indianapolis, Ind.
27. Jacksonville, Fla.
28. Jersey City, N.J.
29. Kansas City. Ho.-Ks.
30. Los Angeles-Long Beach, Ca.
31. Louisville, Ky.-Ind.
32. Memphis, Teim.-Ark.
33. Miami, Fla.
34. Milwaukee, Wls.
35. Mlnneapolls-St. Paul, Minn.
36. Nashville-Davidson, Tenn.
37. Mew Orleans, La.
38. New York, N.Y.
39. Newark, N.J.
40. Norfolk-Portsmouth, Va.
41. Oklahoma City, Okla.
42. Omaha, Nebraska-Iowa
43. Paterson-Cllfton-Passalc, N.J.
44. Philadelphia, Pa.-N.J.
45. Phoenix, Ariz.
46. Pittsburgh, Pa.
47. Portland, Oreg.-Wash.
48. Provldence-Pawtucket-Warwlck, R.I.-
Mas s.
49. Richmond, Va.
50. Rochester, N.Y.
51. Sacramento, Ca.
52. St. Louis, Mo.-111.
53. Salt Lake City, Utah
54. San Antonio, Texas
55. San Bemadlno-Rlverslde-Ontarlo, Ca.
56. San Diego, Ca.
57. San Francisco-Oakland, Ca.
58. San Jose, Ca.
59. Seattle-Everett, Wa.
60. Sprlngfleld-Chlcopee-Holyoke,
Mass.-Conn.
61. Syracuse, N.Y.
62. Tampa-St. Petersburg, Fla.
63. Toledo, Ohio-Mich.
64. Washington, D.C.-Md.-Va.
65. Youngs town-Warren, Ohio
A • Outstanding (a x + s)
B • Excellent (x + .28a z B < x + a)
C - Good (S - .28s < c < x + .28s)
D • Adequate (S-s
-------
RANK
CHART 2
REGIONAL VARIATIONS IN INDEXES; POLITICAL COMPONENT (L)
SMSA ADJUSTED STANDARDIZED SCORE
r i
2
3
4
5
A / 6
R ^
*
C<
D<
E \
7
8
9
10
11
.12
rl3
14
15
16
17
18
19
20
21
22
23
24
25
26
27
r28
30
31
32
33
34
35
36
37
38
1,39
f40
41
42
43
44
45
46
47
48
49
i.50
f51
52
53
54
55
56
57
58
59
60
61
62
63
64
i 65
ffolo, NY
Albony - Schenectody - Troy, NY
Rochester, NY
Syracuse. NY
Grand Rapids, Mich
Hartford. Conn
Sacramento, Calif
Portland, Oreg - Wash
Minneapolis- St.Paul. Minn
Boston. Moss
Salt Lake City. Utah
Milwaukee, Wis
Detroit, Mich
Pittsburgh, Pa
San Diego. Calif
Denver, Colo
Anaheim - Santa Ana - Garden Grove, Calif
Providence - Pawtucket - Warwick, Rl - Mass
Seattle - Everett. Wash
Toledo. Ohio - Mich
Columbus, Ohio
Newark, NJ
Chicago, III
Son Francisco - Oakland, Calif
Son Jose, Calif
Cincinnati, Ohio - Ky - Ind
Oklahoma City, Okla
Cleveland, Ohio
Youngstown - Warren, Ohio
San Bernodino - Riverside - Ontario, Calif
Springfield - Chicopee - Holyoke, Mass - Conn
Akron, Ohio
Omaha, Nebr - Iowa
St.Louis, Mo - III
Dayton, Ohio
Baltimore, Md
Los Angeles - Long Beach, Calif
Allentown - Bethlehem - Boston, Pa - NJ
Richmond, Va
Philadelphia, Pa - NJ
Indianapolis, Ind
Louisville, Ky - Ind
Washington, DC - Md - Va
Gary - Hammond - East Chicago, Ind
New York, NY
Honolulu, Hawaii
Fort Lauderdale - Hollywood, Fla
Jersey City, NJ
Nashville - Davidson, Tenn
Kansas City, Mo - Ks
Tampa - St. Petersburg, Fla
Norfolk - Portsmouth, Va
Houston, Texas
Mioni, Fla
Phoenix, Ariz
Atlanta, Ga
Peterson - Clifton - Passalc, NJ
Greensboro - Winston - Salem - High Point, NC
Memphis, Tenn - Ark
Fort Worth, Texas
Jacksonville, Flo
Birmingham, Ala
New Orleans, La
Dallas, Texas
San Antonio, Texas
X- S
x-s
X +S
X = Meon = 2.6219
S = Stondard Deviation = . 6466
106
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In terms of funds from the Federal Government, local government in
Buffalo did not show a strong position in sharing the power. Only
1.8 percent of all local government revenues came from the Federal
Government, as compared to 2.7 percent in the U.S. and 8.3 percent in
San Antonio. Grand Rapids, Michigan, another outstanding SMSA in the
political component, showed the worst bargaining power with the Federal
Government--revenues from the Federal Government consisted of only
0.5 percent.
Albany/Schenectady/Troy, New York, and Allentown/Bethlehem/Easton,
Pennsylvania/New Jersey, were the safest SMSA's in 1970, with a
violent crime rate as low as 133 cases per 100,000 population in that
year or about nine and six times, respectively, better than the two
worst areas: New York (1,357 cases per 100,000) and Baltimore (957
cases per 100,000). Other safe areas were Milwaukee, Syracuse, Honolulu,
and Rochester. The high violent crime' areas in 1970, as shown in
Table A-2 in the Appendix, were Miami, Los Angeles, Detroit, Jacksonville,
Chicago, and Washington, B.C. For property crime, Denver dominated all
large SMSA's, with 4,611 cases per 100,000 population in that year.
Following Denver are Los Angeles, San Francisco/Oakland, Miami, Phoenix,
and Sacramento having property crime rates of over 4,000 cases. Areas
with the lowest violent crime rate also have the lowest property crime
rate.
Crime data are often considered suspect. One reason is that police
officers see the usefulness of clerical work in terms of whether it can
be used for later case documentation. "If there is no likelihood of
finding a suspect, the police often consider filling out a report a
waste of time."2.' Another reason for misleading crime data is that
victims, because of personal reasons, do not always report crimes to
the police. The above findings are very much the same as those found
in other studies using different indexes and weighting schemes.—
Concerning crime prevention, suggestions have been made that the city
or state in which the crime occurred should be held responsible for
compensating the victim. Under present laws the private cost of crime
9/ See Council of Municipal Performance, City Crime (Municipal Perform-
ance Report, 1:1, May-June 1973), p. 25.
10/ For instance, see Council of Municipal Performance Ibid., and The
Wealth of Cities (Municipal Performance Report, 1:3, April 1974),
p. 42; and M. J. Flax, op. cit.
107
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is borne by the individual and he has little hope of being compensated.
Even if the attacker is caught and jailed, the victim ends up paying
part of his own taxes for the prisoner's room and board. Presently
five states—New York, California, Hawaii, Maryland, and Massachusetts--
provide some liability which is not in any form significant compensation.
"Crime costs. So does crime prevention, but the latter also has
benefits to society which can be weighted in the making of decisions
about law enforcement methods and expenditures," stressed North and
Miller.— After a crime occurs, the victim is all too often quickly
forgotten. Our criminal justice system owes the crime victims far better
treatment than they now receive in most cities. As a result of these
criticisms, the Sacramento Police Department will create a position of
Victims Advocate to work with the police and other law enforcement and
medical agencies. The Portland, Orego^Rape Victim Advocate Project
received a 2-year grant of $124,000 to assist the rape victim.H'
The geographical distribution of the SMSA's with outstanding or "A"
rating of political quality of life can be clearly visualized from
Figure 2. Like the patterns revealed in the economic component, they
are concentrated in the northern part of the Middle Atlantic and the
East North Central Region. The most significant or critical finding in
the South Atlantic and East South Central regions is that the sub-
standard SMSA's are clustered there. Therefore, the political quality
of life that each resident faces in these areas of the South may be
completely different from the economic quality. Dallas, Houston,
Fort Worth, and Atlanta received stars in the economic component but
are all in black squares in the political component evaluation. In
other words, while high positive correlation between economic and
political quality are found in the Middle Atlantic and the East North
Central regions, high negative correlation between the two components
is also observed in the SMSA's in the South. The negative correlation
implies that people in those SMSA's are economically healthy and able
to enjoy a good quality of life, but politically their efforts to im-
prove local government professionalism, to inform citizens for political
involvement and participation, and to provide social welfare assistance
to the needy tend to be relatively insufficient and substantially behind
11/ Douglas North and Roger Miller, The Economics of Public Issues
(New York: Harper and Row, 1973), p. 124.
See Patrice Horn (ed.), Behavior Today, Volume 61, Number 5,
(February 3, 1975)
108
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o
ex
o
CO
o
CD
60
d
•H
4-J
CO
ftS
c
o
•H
4-1
3
S-i
4-1
cn
•H
Q
O
•r-l
CL
O
OJ
O
QJ
l-l
D
oo
•H
Pi
A
M *?
0 J
^
•JLr Outstanding
0 Excellent
G
T3
o
o
o
D
A Adequate (D
1 Substandard
109
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their economic status. In Boston, where the economic component is
substandard and the political component outstanding, governments may
gain in popularity if they will stress regional economic growth.
The regional variations in political indexes among the large EMSA's are
shown in Chart 2. This bar chart shows relatively smaller variations
among regions than does the bar chart for the economic component. The
coefficient of variation of the political component is 0.25 (0.6466/
2.6219), as compared to 0.32 for regional economic variation.
As pointed out previously, many indicators used in this component are
related to the central cities in the metropolitan areas rather than
for the entire £MSA, such as the salary figures and the newspaper
circulations. Thus, the results presented in this section should be
interpreted and used with caution.
Crittenden, in a comparative state politics and political system analysis,
has observed that political participation is strongly correlated with
high education and high income. In terms of "welfare orientation"
or "liberalness," Hofferbert confirmed the findings by Dawson and
Robinson that as a state becomes industrialized, the life styles of its
inhabitants naturally create a set of claims for action which are re-
flected in government activity. The governments in the industrialized
states in turn actively respond to the claims. As a result, the States
of New York, Connecticut, California, New Jersey, Wisconsin, Massachu-
setts, Oregon, Minnesota, Wyoming, and Illinois were ranked the highest
10 in welfare orientation in this country. In an inquiry about the
process of diffusion of ideas for news services or programs among the
American states, Walker found that some states adopted political
innovations much more rapidly than others in policy decision making.
In this category, he cited New York, Massachusetts, California,
New Jersey, Michigan, Connecticut, Pennsylvania, Oregon, Colorado, and
Wisconsin. Although Sharkansky argued that economic activity has
substantial influence on public policy, he asserted that regional
110
-------
phenomena make a significant contribution to the explanation of inter-
state differences in policy. Regional affiliations of the states
showed important relationships with most policy decisions.—'
The findings in this section tend to concur in a varying manner with
those earlier studies relating state economy and regionalism to politi-
cal divisions. However, a comparison between this metropolitan study
and other earlier state studies by Liu, Wilson, and the Citizens Con-
ference on State Legislatures leads one to reject quickly the hypothesis
that states which rate low in political activities can have highly
rated regions in the state. The states in the South were rated unfavor-
ably in political quality in all three studies of varying definitions
and measurements. The metropolitan areas in these southern states are
no exception. This is in contrast to the findings in the preceding
section on economic conditions.—'
ENVIRONMENTAL COMPONENT
The concern over the dependence of the human community on the natural
environment and the exchanges and flow of food, materials, energy,
pollution, and the quality of life between man and nature has been our
focal point and the central issue in the past several years. There is
growing dissatisfaction over land use, natural resources extraction,
and pollution damage to our natural environment by industrialization
and urbanization. According to the estimate of the Council on Environ-
mental Quality, a total of $200 billion will be spent on pollution
13 / See John Crittenden, "Dimensions of Modernization in the American
States," American Political Science Review, Volume 61, Number 4,
(1967), pp. 989-1,001; Richard Hofferbert, "The Relation Between
Public Policy and Some Structural and Environmental Variables in
the American States," American Political Science Review, Volume 60,
Number 1. (1966), pp. 73-82; Jack Walker, "The Diffusion of In-
novations Among the American States," American Political Science
Review (September 1969), pp. 880-899; and Ira Sharkansky,
"Regionalism, Economic Status, and the Public Policies of Ameri-
can States," The Social Science Quarterly (June 1968), pp. 9-25.
1.4 / See Ben-chieh Liu, The Quality of Life in the U.S., 1970, op.cit.,
p. 19; John Wilson, The Quality of Life in America (Kansas City:
Midwest Research Institute, 1967), pp. 10-11; and Citizens Con-
ference on State Legislatures, State Legislatures: An Evaluation
of Their Effectiveness (New York: Praeger Publishers, 1971),
p. 83.
Ill
-------
control between now and 1980, in order to maintain present air and water
quality standards.—' Since resources are finite and environmental pro-
tection or pollution control is costly, it is necessary to ascertain
that the last unit of control bought imposes no additional costs greater
than the additional benefits.
Kneese clearly stated that given the population, industrial produc-
tion, and transport service in a regional economy, it is possible to
visualize combinations of social policy which could lead to quite
different relative burdens placed on the various residuals--receiving
environmental media and tools need to be selected and developed which
can be used to approximate optimal combinations of the environmental
protection.—' The precondition for any effective and efficient policy
combination in environmental protection, however, is a set of well-
designed and meaningful environmental indicators which not only can
directly reflect the well-being of the environment in which people live,
but also can provide a yardstick for measuring the changes over time.
Thus, the mandate by the National Environmental Protection Act of 1969,
charged the Council on Environmental Quality with preparing a set
of indicators to measure the state of the environment for the nation.
As a result, the relative indicators have been published annually by
the Council on Environmental Quality. Nevertheless, these indicators
do not exist for all metropolitan areas in a comparable form, nor has
a systematic framework been established to fulfill the requirement of
developing a comparable set of indicators among regions. This section
represents an exploratory effort devoted to such an establishment.
The environmental quality of life indicators in this study concern
both individual and institutional environment and the natural environ-
ment. Air, visual, noise, water, and solid waste pollution are by-
products of the postindustrialized society. Their existence and the
attempts at eradication not only impose a heavy financial burden on our
society, but they are also hazards to human health, animal fertility,
157 See President's Council on Environmental Quality, Environmental
Quality 1972: Third Annual Report (Washington, D.C., 1972).
16/ Allen Kneese, "Analysis of Environmental Pollution," The Swedish
Journal of Economics (March 1971).
112
-------
crop production, etc.— Thus, relative indicators for these five
categories were constructed based on the absolute indicators obtained
from various public and private sources. The individual and institu-
tional environment among the metropolitan areas is evaluated jointly
on 10 different factors.
The natural environment is evaluated from five climatological and two
recreational factors. The factors included in this component are fewer
than desirable and are far from being complete because of the lack of
empirical statistics. Nevertheless, these factors provide basic
information for a fairly accurate judgment on urban environment for all
metropolitan areas.
All adjusted standardized scores in the environmental component have
negative values because most factors used are "environmental bads"
rather than "environmental goods." Since most of the factors are
hazardous to life, the quality of life would be the higher given
smaller intakes of the environmental bads. According to Table 3,
Sacramento, California,had the best environment in 1970, with an index
of -0.20; Seattle/Everett and Miami are rated, respectively, second and
third. The remaining "A" rated SMSA's are Honolulu, San Bernadino/
Riverside/Ontario, San Diego, San Jose, Phoenix, Allentown/Bethlehem/
Easton, Springfield/Chicopee/Holyoke, and Portland.
People in Sacramento have the longest trail mileage—or about 2 miles
per 1,000 people--and the manufacturing industries in the area generated
tht> least solid wastes—only 350 tons per million dollar value added.
(See Table A-3 in the Appendix.) The trail mileages were aggregated
from the county data of the first survey of the U.S. Bureau of Outdoor
Recreation, and the solid waste generation was computed from a regres-
sion model. Both data are subject to the question of source reliability.
Specifically, every aspect of urban life generates solid wastes, and the
use of industrial solid wastes as an indicator for all household, com-
mercial, municipal, and other solid wastes may be biased and misleading.
For instance, L. D. Zeidberg, R. A. Prindle, and E. Landau pointed
out that 25 to 50 percent of the total morbidity can be associ-
ated with air pollution. Hence, Lave and Seskin estimated the
cost of air pollution, because of health effects, would run
between $14 and $29 billion per year. See Lester Lave and
Eugene Seskin, "Air Pollution and Human Health," Science,
Volume 169 (August 21, 1970), pp. 723-733.
113
-------
TABLE 3
INDEX AND RATING OF ENVIRONMENTAL COMPONENT (L)
1.
2.
3.
4.
5.
6.
7.
1.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
43.
44.
45.
46.
47.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
8MSA
Akron, Ohio
Albany-Schenectady-Troy, N.Y.
Allentown-Bethlehem-Easton, Pa. -N.J.
Anaheim-Santa Ana-Garden Grove, Ca.
Atlanta, Ga.
Baltimore, Md.
Birmingham, Ala.
Boaton, Haas.
Buffalo, N.Y.
Chicago, 111.
Cincinnati, Ohlo-Ky.-lnd.
Cleveland, Ohio
Columbua, Ohio
Dallas, Texaa
Dayton, Ohio
Denver, Colo.
Detroit, Mich.
Fort Lauderdale-Hollyvood, Fla.
Fort Worth, Texas
Gary-Hammond-East Chicago, Ind.
Grand Rapids, Mich.
Greensboro-Winston-Salem-Hlgh Point ,
N.C.
Hartford, Conn.
Honolulu, Hawjli
Houaton, Texaa
Indianapolis, Ind.
Jacksonville, Fla.
Jersey City, N.J.
Kansas City, Ho.-Ks.
Los Angeles-Long Beach, Ca.
Louisville, Ky.-Ind.
Memphis, Tenn.-Ark.
Miami, Fla.
Milwaukee, Wls.
Nashville-Davidson, Tenn.
New Orleans, La.
New York, N.Y.
Newark, N.J.
Norfolk-Portsmouth, Va.
Oklahoma City, Okla.
Paterson-Cllfton-Paasaic, N.J.
Philadelphia, Pa. -N.J.
Phoenix, Ariz.
Pittsburgh, Pa.
Portland, Oreg.-Wash.
Mass.
Richmond, Va.
Rochester, N.Y.
Sacramento, Ca.
St. Louis, Mo. -111.
Salt Lake City," Utah
San Antonio, Texas
San Be mad ino -River side -On tario.Ca.
San Diego, C>.
San Francisco-Oakland, Ca .
San Jose, Ca.
Seattle-Everett, Wa.
Springfield-Chicopee-Holyoke,
Mass. -Conn.
Syracuse, N.Y.
Tampa-St. Petersburg. Fla.
Toledo, Ohio-Mich.
Washington, D C.-Md.-Vs.
Youngstown-Warren, Ohio
Ad lusted
Value
-0.9667
-1.2917
-0.6167
-1.0500
-1.2833
-1.2667
-1.4250
-1.2500
-1.2000
-1.8167
-1.0333
-1.4250
-1.0917
-6.9083
-1.3167
-0.9917
-1.7250
-1.0833
-0.8583
-1.1750
-1.0333
-1.3000
-1.1250
-0.4583
-1.0000
-1.5250
-1.2500
-1.0167
-1.1250
-1.0583
-1.4167
-1.2083
-0.4167
-1.0417
-1.0833
-1.2667
-1.3333
-1.2000
-0.8667
-0.8250
-1.0000
-1.0250
-0.5917
-1.8667
-0.6500
-0.7667
-1.1333
-0.7000
-0.2000
-1.5833
-1.0250
-0.8333
-0.4750
-0.5333
-0.7000
-0.5333
-0.2667
-0.6167
-1.1500
-1.0583
-1.1833
-0.8333
-0.9667
Standard laei
Sink
23
53
9
33
52
50
S9
48
45
64
30
60
38
21
56
24
63
36
18
43
31
54
40
4
26
61
49
27
39
34
58
47
3
32
20
37
51
57
46
19
15
25
28
8
65
11
14
41
13
1
62
29
17
5
6
12
7
2
10
42
35
44
16
22
] Scores
Ratine
C
D
A
C
D
D
E
D
D
E
C
E
C
B
D
C
E
C
B
D
C
D
C
A
C
E
D
C
C
C
E
D
A
C
B
C
D
D
D
B
B
C
C
A
E
A
B
D
B
A
E
C
B
A
A
B
A
A
A
D
C
D
B
C
St»nd«rdized Score•
Value
0.0340
-0.1209
0.1631
0.1063
-0.0811
-0.0787
-0.3185
-0.2825
-0.0388
-0.4576
-0.0656
-0.4553
-0.0184
0.0258
-0.1892
-0.0514
-0.5801
0.1103
-0.0031
-0.0655
0.0358
-0.1628
-0.0647
0.1648
-0.0114
-1.0332
-0.1441
-0.0482
-0.0642
0.0957
-0.1389
-0.0160
1.5154
-0.0245
0.0776
-0.0244
-0.1624
-0.1289
-0.1504
0.1278
0.0009
-0.1279
0.0070
-0.0050
0.1192
-0.8436
0.2040
0.1308
-0.0072
0.2366
1.2102
-0.2920
-0.1141
0.0892
0.4583
0.2624
0.2163
0.3292
0.4327
0.3035
-0.0302
-0.1041
-0.0712
0.0991
0.0203
Rank
23
49
12
17
46
45
60
58
37
62
43
61
33
24
57
39
63
16
28
42
22
56
41
11
31
65
53
38
40
19
52
32
1
35
21
34
55
51
54
14
27
50
26
29
15
64
10
13
30
8
2
59
48
20
3
7
9
5
4
6
36
47
44
18
25
Bating
C
D
B
B
C
C
D
D
C
E
C
E
C
C
D
C
E
B
C
C
C
D
C
B
C
E
D
C
C
C
D
C
A
C
C
C
D
D
D
B
C
D
C
C
B
E
B
B
C
B
A
D
D
C
A
B
B
B
A
B
C
D
C
B
C
A - Outstanding (2 x" + s)
B - Excellent (x + 0.28s s. B < x + s)
C - Good (x - 0.28s < C < if + 0.28s)
D - Adequate (Z - s < D •- * - 0.28»)
E - Substandard (4 x - a)
Mean (x) - -1.0342
Standard Devlatlon(a) - 0.3452
Mean (x) - 0.0000
Standard Devtatlcn(s) - 0.3491
114
-------
Furthermore, the waste multiplier of 7.6 tons per manufacturing employee
per year is only an aggregate figure with no consideration whatsoever
of different types of manufacturing industry. The solid waste indicator
in this study only implies that for each million dollars worth of value
added by manufacturing industries, the fewer workers employed, and hence, the
fewer tons of solid wastes generated according to the formula, the
better.
Although Sacramento ranked first in the environmental component, this
does not mean that it has all the best in every environmental category.
For instance, it had nearly the worst noise problem in that year
because of its high motorcycle and vehicle registration per 1,000 popu-
lation and high population density in the central city. Admittedly,
these are only crude indicators of noise pollution, which in reality
depends on the number of motorcycles and vehicles used per day, and
their capacity of noise generation such as the age, size, etc. In
comparison, Miami SMSA had the best natural environment and had virtually
no visual pollution, but its water pollution and solid waste problems
were considerably worse than most SMSA's under discussion. Seattle/
Everett SMSA had very little air, visual, and water pollution, but its
noise pollution was worse than average.
Environmental problems were most serious in the East North Central
region. Pittsburgh scored the lowest among the 65 SMSA's with an index
value of -1.87. Chicago and Detroit followed closely with an index of
-1.82 and -1.72, respectively. The other five SMSA's rated substandard
are St. Louis (Missouri and Illinois), Indianapolis, Indiana; Cleveland,
Ohio; Birmingham, Alabama; and Louisville (Kentucky and Indiana). While
noise pollution did not seem to be a problem in Pittsburgh, the worst
water pollution, plus very serious air and visual pollution, push the
rating for Pittsburgh down to the bottom. For instance, the mean
level for sulfur dioxide in Pittsburgh was 63.0 ppm, lower only
than Cleveland (113.0 ppm) and Providence/Pawtucket/Warwick (64.0 ppm);
the water pollution index was 48.0 for Pittsburgh, substantially higher
than the second and the third worst SMSA's of Detroit (31.06) and
Boston (24.00), and much higher than the majority of the SMSA's with
indexes ranging from 0.68 (Anaheim/Santa Ana/Garden Grove) to 9.78
(Columbus). People in both Chicago and Detroit suffered seriously
from the air and water pollution; however, people in Detroit enjoyed
a relatively better natural environment and saw fewer dilapidated housing
units than citizens in Chicago. St. Louis was observed to have little
solid waste problem, but its very small park and recreational area
(2.3 acres per 1,000 people) and bad climatological data forced its
rating down.
115
-------
Figure 3 contrasts vividly with Figure 1 in the East North Central
region: the economic core of the industrial belt of this country has
the worst pollution and environmental problems. This demonstrates
clearly the trade-off between industrial growth and environmental health.
Except in Birmingham, which was also troubled by air and visual pollu-
tion as well as climatological conditions, the environment in the
South has been kept in adequate or good condition probably because little
trading occurred between economic goods and environmental bads. The
West Coast, on the other hand, is the only region in this country which
has enjoyed concurrently both a prosperous economy and beautiful
environment—probably due to public awareness of and proper planning
to protect the environment.
Regional variation in index values was high for 1970; the coefficient
of variation was 0.33. This high coefficient of variation, however,
can be attributed largely to the extreme values in both the outstanding
and the substandard SMSA's. As portrayed in Chart 3, very small
variations among environmental indexes exist for the majority of U.S.
urban areas. This indicates that urban environmental problems have
not been significantly different among most of the SMSA's. Even at the
bottom of the scale, the SMSA's rated "E" are fewer than in the economic
and the political components. In fact, only the last five SMSA's in
the chart showed significant deviation from an adequate level and thus
require some special consideration. The air pollution concentration
level has been, on the average, reduced by some 50 percent in the past
few years in this country because of the efforts of the Environmental
Protection Agency and the public awareness of environmental problems.
Continuing emphasis on cleaning and protecting the environment will
undoubtedly improve environmental quality and thus enrich future urban
life. The rank-order correlation coefficient between the two sets of
rankings is also high, i.e., 0.93, meaning that the two methods differ
only slightly.
Plans for reduction of air pollution have centered on the improvement of
individual and institutional environments. However, there is much to
be done in our natural environment. Land use is the starting point
for most of man's polluting activites, and land dedicated to parks and
recreational areas makes a significant contribution to environmental
quality in at least two ways. It is enjoyable both in and of itself,
and also for the relief it provides from surrounding and polluting land
uses. The greatest contribution the cities could make to improve
their quality of life may be the acquisition of as much desirable land
as possible, as early as possible, before land prices soar out of
range, or development occurs causing permanent loss of open spaces
116
-------
CO
M
a
•H
PS
c
O ^-'
•H
4-J -P
3 a
Jd
-------
CHART 3
REGIONAL VARIATIONS IN INDEXES; ENVIRONMENTAL COMPONENT (L)
SMSA
ADJUSTED STANDARDIZED SCORE
B <
\ Sacromento, Cotif
2 Seattle-Everett, Wash
3 Miami. Flo
4 Honolulu, Ho.
5 San Bernodino-Riverside-Ontorio. Calif.
6 San Diego. Colif.
7 Son Jose. Colif
8 Phoenix. Ariz.
9 Allentown-Bethleherrr-Easton, Pa.-N.J.
10 Springfield-Chicopee-Holyoke, Moss.-Conn.
,11 Portland. Oreg.-Wash
' 12 Son Francisco-Oakland. Colif.
13 Rochester. N.Y.
14 Providence-Pawtucket-Worwick, R.I.-Moss.
15 Oklohomo City, Okla
16 Washington, D C.-Md -Va.
17 San Antonio, Texas
18 Fort Worth, Texas
19 Norfolk-Portsmouth, Vo.
20 Minneapolis-St. Paul. Minn.
.21 Dallas. Texas
" 22 Youngstown-Warren, Ohio
23 Akron. Ohio
24 Denver, Colo
25 Paterson-Clifton-Possaic. N.J.
26 Houston, Texas
27 Jersey City, N.J.
28 Philadelphia, Pa.-N.J.
29 Salt Lake City, Utah
30 Cincinnati, Ohio-Ky .-Ind.
31 Grand Rapids, Mich.
32 Milwaukee. Wis.
33 Anaheim-Santo Ana-Garden Grove, Colif.
34 Los Angeles-Long Beach, Calif.
35 Tampa-St.Petersburg, Flo
36 Fort Lauderdale-Hollywood, Flo.
37 Noshville-Dovidjon, Tenn.
38 Columbus, Ohio
39 Kansas City, Mo.-Kan.
„ 40 Hartford. Conn
' 41 Richmond, Va.
42 Syracuse, N.Y.
43 Gory-Hammond-East Chicago, Ind.
44 Toledo, Ohio-Mich.
45 Buffalo, N.Y.
46 Newark, N.J.
47 Memphis. T«nn.-Ark
48 Boston. Mass.
49 Jacksonville. Flo
50 Baltimore, Md
51 New Orleans. La.
52 Atlanta, Ga.
53 Albony-Schenectady-Troy, N.Y.
54 Greensboro-Winston-Salem-High Point, N.C.
55 Omaha, Nebr.-lowa
56 Dayton, Ohio
57 New York, N.Y.
58 Louisville, Ky -Ind.
59 Birmingham, Ala.
60 Cleveland, Ohio
61 Indianapolis, Ind.
Mo -III
lich
X+S
C <
/ 61 Indianapolis.
] 62 St.Louis. Mo
I 63 Detroit, Mich
I 64 Chicago, III
-------
18/
and green land.— The need for open space and green land in the metro-
politan areas becomes more urgent as the percentage of American popula-
tion in these areas continues to increase.
The availability of open space and green land as reflected by parks and
recreational areas varies significantly among large SMSA's. The
statistics in Table A-3 in the Appendix reveal that people in Jersey
City had for small parks and recreational areas only 1 acre per 1,000
population in 1970 as compared to 447.2 acres per 1,000 in Miami, 130.1
acres per 1,000 in Sacramento, 116.3 acres per 1,000 in Phoenix, and
48.1 acres in Denver. Almost one-half of the 65 large SMSA's had
fewer than 10 acres per 1,000 population. The Citizen's Advisory
Committee on Environmental Quality has urged that land and water conser-
vation funds be used for urban recreational programs, especially some
outreach programs and a substantial reordering of priorities on federal
aid to recreation.
One of the suggestions regarding our land use pattern and natural
environment conservation is the planned suburban community. A study
by the Real Estate Research Corporation stated that planned suburban
communities with population densities slightly higher than those in
existing new towns can cut capital costs, energy consumption, and pol-
lution by a significant amount.— In terms of environmental, economic,
and energy costs, planned development of all densities is less costly
to create and operate than is sprawl. Nevertheless, higher density
communities will suffer from increased crime, noise, and diminished
privacy. Therefore, the need for a land use plan which optimizes
our natural environment utilization and balances social benefits with
social costs is apparent in metropolitan and suburban expansion.
HEALTH AND EDUCATION COMPONENT
The term "quality of life" is something that everyone can talk about
but no one can define precisely. Diffuse as the term becomes, few
can deny that health and education forms a significant part of it. As
18/ This suggestion was made clear by the Citizen's Advisory Com-
mittee on Environmental Quality; see CACEQ, Annual Report to
the President and to the Council on Environmental Quality 1972
(Washington, D.C.: Government Printing Office, 1972), pp. 20-27.
19/ See Real Estate Research Company, The Costs of Sprawl (Chicago:
Real Estate Research Company, 1974).
119
-------
mentioned earlier, Cantril and Rolls found that good health dominated
all other concerns when they questioned individuals in this country in
both the 1959 and 1971 surveys about their personal hopes. Similarly,
good health was considered their number one hope by respondents in
West Germany, Brazil, the Philippines, and Cuba. Ill health worried
everyone most among respondents in Yugoslavia, Israel, Egypt, and
207
Panama. — No wonder health was selected by the Organization on
Economic Cooperation and Development to be the first in the list of
fundamental social concerns common to most member countries.
Using cross-sectional sample observations from sixth grade pupils,
teenagers, university students, alcoholic patients, mental patients,
and other persons, Scott obtained a unanimous conclusion from the 880
respondents that death is the saddest event, despite the fact that these
groups selected different occasions for the happiest event.—' As a
result, the individual health factor consists of mortality rates for
the general population as well as for infants.
The community health conditions in the study are depicted by medical
care availability—an input factor—in contrast to the mortality rates
for the individual--an output factor. The five community health
factors were chosen to represent, respectively, the medical care man-
power, facility, the rate of utilization, and the public decision on
health provision. The emphasis here is on preventing the occurrence
of health disabilities and the avoidance of disease. The mortality
rates were selected to reflect the level of health quality. Similar to
the income and wealth factors employed in the economic component, both
flow (mortality rate) and stock (medical care availability) variables
are contained in this health component as input to our overall quality
of life regardless of their conventional input-output characteristics.
Improvement in the quality of life necessitates improvement in the
quality of human capital. While health constitutes physical quality of
the human capital, the mental quality of human capital can be primarily
enriched through education and experience. To evaluate the quality of
human capital, the aggregate level of educational attainment of people
in a community and the magnitude of similar educational background among
them are deemed fundamental measurements for it. Although there is
207 See Hadley Cantril, The Pattern of Human Concerns, op. cit.
21/ See Edward Scott, An Arena for Happiness (Springfield, Illinois:
Charles C. Thomas Publishing, 1971), p. 107.
120
-------
evidence that individuals can become less content and happy as their
level of education increases, this individual observation is character-
ized over time and, hence, is of no concern in this static study of
cross-sectional comparison. As a joint product in a collective sense,
however, a community with many highly educated people is generally
preferred to another without. In addition, a community consisting of
residents of homogeneous cultural and educational background is normally
assumed to be better than another comprising members of heterogeneous
cultural and educational attainments. This hypothesis is analogous to
that as postulated by some new welfare economists that total expected
social welfare among individuals would be maximized if their incomes
were equally distributed.
The index and ratings of the health and education component are shown
in Table 4. Of the 13 outstanding SMSA's, the Pacific region
accounted for six and the State of California contained four. San Jose
SMSA had the highest quality of health and education. The composite
index value for San Jose was 2.72 or 2.4 times as high as the metropoli-
tan mean. The 12 other outstanding SMSA's are Salt Lake City, Denver,
San Francisco/Oakland, Hartford, Seattle/Everett, Minneapolis/St. Paul,
Sacramento, Portland, Washington, B.C.; Anaheim/Santa Ana/Garden Grove,
Boston, and Rochester. From the other end of the scale are 11 sub-
standard SMSA's led by Jersey City, Providence/Pawtucket/Warwick,
Birmingham, Tampa, and Norfolk/Portsmouth.
San Jose surpassed other SMSA's in individual health and education
conditions and ranked second in community educational attainment. Al-
though the community health conditions in terms of medical care avail-
ability were outstanding for San Jose, it ranked only 12th in this
category. In a like manner, Salt Lake City outstripped all large
SMSA's except San Jose in individual health and education conditions,
but fell behind in providing medical care services to the community,
ranking only 38th in terms of available physicians, dentists, hospital
beds, etc. New York was rated the best in community medical care avail-
ability with the highest number of physicians and dentists per 100,000
population (286 and 96, respectively, versus 154 and 59 in the U.S.)
and the highest per capita local government expenditures on health
($8.82 against U.S. average of $2.96). Ironically, New York's death
rate was also very high in 1970, 10.5 deaths per 1,000 population or
one death more than the U.S. average. Among the 15 SMSA's with a
death rate exceeding 10.0, New York ranked sixth. (See Table A-4 in
the Appendix.)
121
-------
TABLE 4
INDEX AND RATING OF HEALTH AND EDUCATION COMPONENT (L)
Adjusted Standarditcd Scores
Standardized Score*
1,
2.
3.
4.
f
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
SHSA
Akron, Ohio
Albany-Schenectady-Troy, N.Y.
Allentown-Bethlehem-Easton, Pa.-N.J.
Anaheim-Santa Ana-Garden Grove, Ca.
Atlanta, Ga.
Baltimore, Md.
Birminghria, Ala.
Boston, Mass.
Buffalo, N.Y.
Chicago, 111.
Cincinnati, Ohio-Ky.-Ind.
Cleveland, Ohio
Columbus, Ohio
Dallas, Texas
Dayton, Ohio
Denver, Colo.
Detroit, Mich.
Fort Lauderdale-Hollywood, Fla.
Fort Worth, Texas
Cary-Hamnond-East Chicago, Ind.
Grand Rapids. Mich.
Greensboro-Wins ton-Salem-High Point,
H.C.
Hartford, Conn.
Honolulu, Hawaii
Houston, Texas
Indianapolis, Ind.
Jacksonville, Fla.
Jersey City, N.J.
Kansas City, Ho.-Ks,
Los Angeles-Long Beach, Ca.
Louisville, Ky.-Ind.
Memphis, Tenn.-Ark.
Miami, Fla.
Milwaukee, Wls.
Mlnneapolis-St. Paul, Minn.
Nashville-Davidson, Tenn.
New Orleans, La.
New York, N.Y.
Newark, N.J.
Norfolk-Portsmouth, Va.
Oklahoma City, Okla.
Omaha, Nebraska- Iowa'
Pateraon-Clifton-Passaic, N.J.
Philadelphia. Pa.-N.J.
Phoenix, Ariz.
Pittsburgh, Pa.
Portland, Oreg.-Wash.
Providence- Pawtucket-Warwick, R.I.-
Richmond, Va.
Rochester, N.Y.
Sacramento, Ca.
St. Louts, Mo. -111.
Salt Lake City, Utah
San Antonio, Texas
San Bernadlno-Rlverside-Ontario, Ca.
San Diego, Ca.
San Francisco-Oakland, Ca.
San Jose, Ca.
Seattle-Everett, Wa.
Value
1.1250
1.8625
0.3875
2.0125
0.8375
0.3625
-0.0250
2.0125
1.4250
0.6625
0.6250
1.0875
1.4875
0.7625
1.0625
2.5000
0.9625
0.2000
0.3500
0.7000
1.5375
0.1000
2.2750
1.5375
1.0875
0.6500
0.1125
-0.5250
1.1125
1.7375
0.3125
0.6125
0.6000
1.7000
2.2375
0.6375
0.4250
1.2125
1.2625
0.0625
1.3750
1.7500
1.4625
0.3000
1.6000
0.7875
2.1375
-0.1750
0.4500
2.0000
2.1875
0,5625
2.5625
0.2875
1.3625
1.8125
2.3750
2.7250
2.2625
Rank
30
14
52
11
37
53
63
12
25
42
46
32
23
39
34
3
35
58
54
40
21
60
5
22
33
43
59
65
31
18
55
47
48
19
7
45
51
29
28
61
26
17
24
56
20
38
9
64
50
13
8
49
2
57
27
16
4
1
6
Bating
C
B
D
A
D
D
E
A
B
D
D
C
B
D
C
A
C
E
D
D
B
E
A
B
C
D
I
E
C
B
E
D
D
B
A
D
D
C
C
E
B
B
B
E
B
D
A
E
D
A
A
D
A
E
B
B
A
A
A
Value
0.0718
0.3846
-0.3776
0.7431
-0.0970
-0.4635
-0.7143
0.6282
0.15II
-0.3318
-0.3446
-0.0458
0.2651
-0.2615
-0.0366
0.9190
-0.1208
-0.5872
-0.5269
-0.6149
0.1797
-0.9202
0.5289
0.0121
-0.0824
-0.3626
-0.6149
-1.6011
-0.0186
0.4113
-0.4356
-0.3393
-0.2183
0.4344
0.7331
-0.2440
-O.S696
0.2873
0.0144
-0.6898
0.1734
0.3847
0.1735
-0.4061
0.2778
-0.1372
0.6135
-0.6958
-0.4548
0.5445
0.7818
-0.2646
0.9570
-0.4715
0.1585
0.3203
0.8512
1.6010
0.7010
Rank
2$
18
49
7
36
53
62
10
27
44
47
33
22
41
32
4
37
57
55
59
23
63
13
30
J5
48
58
65
51
16
51
46
39
15
8
40
56
20
29
60
25
17
24
50
21
38
11
61
52
12
6
42
3
54
26
19
5
1
9
Ratim
C
B
D
A
C
D
E
A
C
D
D
C
B
D
C
A
C
E
D
E
B
^
B
C
C
D
E
E
C
B
D
D
D
B
A
D
E
B
C
E
B
B
B
D
B
C
A
E
D
B
A
D
A
D
C
B
A
A
A
60. Springfield-Chlcopee-Holyoke,
Hass.-Conn.
61. Syracuse, N.Y.
62. Taups-St. Petersburg, Fla.
63. Toledo, Ohio-Mich.
64. Washington, D.C.-Md.-Va.
65. Youngstown-Warren, Ohio
A - Outstanding (i x + s)
B - Excellent (x + .28s s. B < x + s)
C • Good (x - .28s < C < x + .28s)
D • Adequate (x-s
-------
Other "A" rated SMSA's such as Seattle/Everett, Sacramento, and Anaheim/
Santa Ana/Garden Grove also showed relatively incomparable positions in
community medical care provision. The remaining "A" rated SMSA's in
this component, however, showed a good balance among individual and
community health and education factors.
Three SMSA's showed negative indexes in this component: Jersey City,
Providence/Pawtucket/Warwick, and Birmingham. The negative indexes
resulted from the fact that the scores of the negative input factors
such as death rate, infant mortality rate, and the percentage of
population 16 to 21 years of age not high school graduates in the
individual conditions category were so low that they more than offset
the positive input factors scores. Table A-4 in the Appendix reveals
the death rate statistics for these three SMSA's, respectively, as
12.2, 10.5, and 10.3 per 1,000 population: the infant mortality rate
as 23.5, 22.5, and 23.0 per 1,000 live births; and the percentage of
males 16 to 21 not high school graduates as 18.0 percent, 17.2 percent,
and 18.9 percent. However, these three SMSA's were relatively better
as far as the community medical care availability is concerned. They
ranked 48th, 37th, and 23rd, respectively, among the 65 large SMSA's.
The geographic distribution of various health and education ratings
among SMSA's is presented in Figure 4. While the West Coast and the
New England region had most "A" rated SMSA's, the "E" rated SMSA's were
scattered in the South and along the East Coast, The State of
California showed extremely well in health and education with no SMSA
in the state rating below excellent or "B." In contrast, three of the
four SMSA's in Florida received less than adequate or "substandard"
ratings. The implication is that the precondition for a good quality
of life in the South would be to invest in human resources by either
expanding the educational programs, improving the health facilities
and medical care availability, or both.
It is of interest that there exists a clear dividing line between states
with outstanding and excellent ratings and those with substandard
ratings. It is surprising to note that two neighboring SMSA's in the
same state received completely opposite ratings. In Massachusetts,
Boston was rated "A" yet Providence/Pawtucket/Warwick ranked 64th.
Apparently, Boston showed better results than the national average in
almost every factor, whereas Providence/Pawtucket/Warwick revealed the
opposite. Given the reliability of the statistics one may question
why, for instance, per capita local government health and educational
expenditures in Boston amounted to $2.9 and $130.7, respectively, but
123
-------
the corresponding figures in Providence/Pawtucket/Warwick were only
$0.9 and $118.4. In addition, one may attempt to seek causes of the
high death rates in the latter SMSA where more than two deaths per
1,000 were recorded in 1970, than in Boston SMSA in both infant and
general death category.
The index values computed for the health and education component for
the 65 SMSA's revealed a very high standard deviation, 0.79, which is
more than two-thirds of the mean, 1.13. The standard deviation reflects
dispersion of scores so that the variability of different distributions
may be compared in terms of the value of the standard deviation. With
a high value of standard deviation and low mean value, the coefficient
of variation thus becomes very large, 0.70, the highest among those
of the quality of life components analyzed so far. Chart 4 demonstrates
visually the wide dispersion of index scores. The implication of this
wide dispersion is, in short, that the health and education conditions
are significantly unequal among urban areas in this country.
The geographic variations in ratings in this section are very consistent
with those of the state studies by Liu and Wilson cited previously. To
be specific, the states that rated very high in health and education
quality are also found to have high ratings for the SMSA's in these
states, and vice versa. In this sense, the state indicators, though
aggregate, may still be good regional indicators for any purpose of
relative static comparison. Furthermore, the correlation coefficient
(r) between the rankings produced by the two methods is very high,
r = 0.98, indicating a great consistency between underlying methods
employed.
While health and educational manpower, facilities, and services are
lacking in some areas, they are in excess in others. There is also
functional as well as geographical maldistribution, causing regional
disparities and imbalanced results in the health and education quality
of life in this country. The market mechanism works imperfectly in
meeting needs for decent health care and adequate educational attainment.
As the Committee for Economic Development pointed out, faulty allocation
of resources is a major cause of inadequacies and inequalities in U.S.
health services, resulting in poor or substandard care for large segments
of the population.
Educational background is also a crucial determinant of the quality of
labor. Mounting evidence suggests that education and advances in
knowledge are critical factors contributing to national income growth
worldwide. For instance, Denison, in an extensively detailed empirical
study, found that about 15.0 percent and 23.0 percent of the U.S.
economic growth rate between 1950 and 1962, were accounted for by
increased education of the labor force and the advances of knowledge.
124
-------
CHART 4
REGIONAL VARIATIONS IN INDEXES: HEALTH AND EDUCATION COMPONENT (L)
RANK
SMSA
HEALTH AND EDUCATION COMPONENT (A)
ADJUSTED STANDARDIZED SCORE
A<
B<
C i
D<
E<
i
8
4
5
6
7
9
10
11
12
.. '3
• 14
15
16
17
18
19
70
21
22
23
24
25
26
.27
29
30
31
32
33
34
35
36
: 37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
32
53
L 54
r 55
56
57
58
59
60
61
62
63
64
Son Jo», Calif
Soil Lake City. Utah
Denver. Colo
San Francisco - Oakland, Calif
Hartford. Conn
Seattle - Everett, Wash.
Minneapolis - St. Paul, Minn
Sacramento, Calif
Portland, Oreg - Wash
Washington. DC - Md - Va
Anaheim - Santo Ana - Garden Grove, Calif
Boston, Mais
Rochester, NY
Albany - Schenectody - Troy. NY
Syracuse. NY
San Diego, Calif
Omaha, Nebr - Iowa
Los Angeles - Long Beach, Calif
Milwaukee, Wi,
Phoenix, Ariz
Grand Ropids. Mich
Honolulu, Hawaii
Columbus, Ohio
Polerson - Clifton - Passoic. NJ
Buffalo, NY
Oklahoma City, Okla
Son Bernodino - Riverside - Ontario. Calif
Newark. NJ
New YorJc, NY
Akron. Ohio
Kansas City. Mo - Ks
Cleveland, Ohio
Houston. Texas
Dayton, Ohio
Detroit, Mich
Toledo, Ohio - Mich
Atlanta, Go
Pittsburgh, Po
Dallas, Texas
Gary - Hammond - East Chicago, Ind
Springfield - Chicopee - Holyoke, Mass - Conn
Chicago, III
Indianapolis, Ind
Youngstown - Worren, Ohio
Nashville - Davidson, Tenn
Cincinnati. Ohio- Ky - Ind
Memphis, Tenn - Ark
Miami, Flo
St. Louis. Mo- III
Richmond, Va
New Orleans, La
Allentown - Bethlehem - Eofton, Pa - NJ
Baltimore. Md
tort Worth, Texos
Louisville. Ky- Ind
Philadelphia, Po - NJ
Son Antonio. Texos
I'ort Louderdale - Hollywood, Flo
Jacksonville, Flo
Greensboro - Winston-Salem - High Point, NC
Norfolk - Portsmouth, Vo
lompo - SI Petersburg, Flo
Birmingham, Ala
Providence - Pawtucket - Warwick, Rl - Mass
Jersey City, NJ
K-s
x-s
X- Meon- 1.1252
S > Standard Deviation < .7868
125
-------
CO
60
4-1
CO X— N
OH I-J
M-H
- O
e
01
c
o
a,
o
o
4J C
CO O
•rH -H
Q 4-1
cfl
O O
•H 3
X! 13
a w
60
o
a>
CO
a>
60
•r-l
A
!'• f
0 J
•:>
^f Outstanding
9 Excellent
/•*-.
0
^^
13
O
O
O
a
-------
In Belgium, the corresponding figures for the same period are 14.0
percent and 25.0 percent; in the United Kingdom, 12.0 percent and
32.0 percent; in Italy, 7.0 percent and 13.0 percent, etc.—' On an
individual basis, Daniere and Mechling utilized data from the 1960
Census of Population and computed discounted lifetime earnings by
occupation for people with 4 years of college and those with education
beyond the graduate level. They found that on the average males with
graduate education would earn 17.0 percent more income than those with
college education--$187,818 against $160,992.^1' In Greece, Psacharo-
poulos estimated the annual labor earnings difference between those
with high school and those with college education was more than 49.0
percent in 1960.M/
In this country, the educational level of the population has been rising
at a remarkable rate for several decades. The median school years
completed among the population 25 years of age and over in 1940 was 8.6;
the figure rose to 9.3, 10.5, and 12.1, respectively, in 1950, 1960,
and 1970.^1' Nevertheless, in 1970, the median school years completed
was relatively lower in many SMSA's than the U.S. average. Examples
are Greensboro/Winston-Salem/High Point, North Carolina--11.1;
Baltimore, Maryland--11.3; and Birmingham--11.4, as compared to the U.S.
average of 12.1 years completed. Improving the quality of education in
the lagging regions will not only strengthen the skill level and earning
potential but will also increase the mobility of individuals in these
regions. Equal opportunity in education itself automatically will
reduce the inequalities in employment and income distributions among
people in this country. Eliminating the gap of educational attainment
among regions will undoubtedly have other significant social benefits,
tangible and intangible.
22/ Edward F. Denison, Why Growth Rates Differ (Washington, D.C.: The
Brooking Institution, 1967).
23/ See Andre Daniere and Jerry Mechling, "Direct Marginal Productivity
of College Education in Relation to College Aptitude of Students
and Production Costs of Institutions," The Journal of Human
Resources, Volume 5, Number 1 (Winter 1970), pp. 51-70.
24/ See George Psacharopoulos, "Estimating Shadow Rates of Return to
Investment in Education," The Journal of Human Resources, Volume 5,
Number 1 (Winter 1970), pp. 34-50.
25/ See U.S. Department of Commerce, Bureau of the Census, Statistical
Abstract of the U.S., 1971 (Washington, D.C.: U.S. Government
Printing Office, 1972), Table 164 on p. 109.
127
-------
SOCIAL COMPONENT
The output of quality of life as perceived by people in any urban area
at a particular time is measured by the physical and psychological
inputs. This study focuses on the physical input measurements. In
the preceding sections measures, findings, and implications have been
discussed for four physical input components of the quality of life in
the large metropolitan areas: the economic component: illustrates the
level and capacity of consumption and production of g;oods and services
to meet the basic human desire for a decent standard of living; the
political component measures the efficiency and performance of local
governments or institutions which provide goods and services for
satisfying basic public needs; the environmental component describes
the quality of both the man-made and the natural environment in which
we live; the health and education component depicts the quality of
human resources or human capital on which not only the existing but
also the future quality of life depends. This section presents the
empirical findings in the social component.
All economic, political, environmental, and health and education factors
are essential attributes to the production of quality of life for any
individual. However, no individual's quality of life can be completely
represented by the four components without the inputs from the social
component. As well demonstrated by Maslow, Scott, and others the arena
for human life is constituted of the self, other people, and the environ-
26 /
ment or community.—' The human quality of life, therefore, has to be
reflected in the quality of self, other people, and the community. The
four components discussed previously cover these three elements in the
human life arena, but the linkage or the interflow relationships among
them has not yet been delineated. The interflow relationships are
considered in this study as the social component.
In the social component, major concerns center on the community living
conditions, the equality among individuals, and the independency of
each individual. In other words, the interflow relationships are
differentiated and reflected first, by factors measuring the level and
potentiality of the development and flourishing of individual indepen-
dence and dignity; secondly, by factors describing the differences
between the actual and desired levels of equality or justice in seeking
26/ Abraham Maslow, Motivation and Personality (New York: Harper and
Row, 1970); and Edward Scott, An Arena for Happiness (Springfield,
Illinois: Charles C. Thomas, 1971).
128
-------
employment and housing, in commanding goods and services, etc., as a
result of race, sex, and spatial discrimination; and thirdly, by
factors portraying desirable living conditions collectively enjoyed by
individuals, such as high level of safety and security, good accessibility
to basic health, commercial, and recreational facilities, and sufficient
opportunities to participate in social, cultural, and sports activities.
Some of the factors chosen in this section may be conventionally re-
garded as input variables and some as output measures, but they are
all physical inputs to our measure of social quality of life. There
are two basic arguments for the exclusion of the conventionally defined
input information from the social indicator approach with emphasis on
output measurement. First, outputs are said to give a more accurate
picture of actual social conditions than do inputs, e.g., educational
attainment may be a better indicator than expenditure per capita.
Second, our understanding about the technical relationships among inputs
and outputs are sedimentary in particular and poor in general; e.g.,
the relationship between number of policemen per 100,000 population
and the crime rate. For this reason this study attempts to balance
empirically the two sets of factors, and, theoretically, they are all
regarded as physical inputs to our quality of life.
The indexes and ratings for the social component are contained in
Table 5. Portland ranks outstandingly as the finest metropolitan area
with an index value of 1.03--1.86 standard deviations above the mean.
Next are Seattle/Everett, Omaha, Denver, and Sacramento, all having
very high index values. In addition, there are seven more outstanding
SMSA's with index values higher than the mean (0.48) plus one standard
deviation (0.29)--San Diego, Oklahoma City, Milwaukee, Minneapolis/
St. Paul, Los Angeles/Long Beach, San Francisco/Oakland, and Kansas City.
Although the New England and Middle Atlantic regions showed unfavorably
in the social component (no "A" rated SMSA) relative to preceding
components, these regions had about one-half of the "B" or excellent
SMSA's. As Figure 5 reveals, almost all large SMSA's west of the
Mississippi River are rated either excellent or outstanding except those
in the State of Texas. In fact, with the exception of Milwaukee, all
12 outstanding SMSA's are west of the Mississippi.
There are 13 SMSA's with substandard ratings; they all are located east
of the Mississippi River and are clustered mainly in the Middle Atlantic
and the East North Central regions. Jersey City and Detroit fall at
the bottom of the list with index values substantially below the metro-
politan average. In fact, they are the only two SMSA's with negative
129
-------
TABLE 5
INDEX AND RATING OF SOCIAL COMPONENT (L)
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
IB.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
12.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
33.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
SMSA
Akron, Ohio
Albany-Schenectady-Troy , New York
Allentown-Bethlehem-Easton, Pennsylvania-New Jersey
Anaheim-Santa Ana-Garden Grove, California
Atlanta, Georgia
Baltimore, Maryland
Birmingham, Alabama
Boston, Massachusetts
Buffalo, New York
Chicago, Illinois
Cincinnati, Ohio-Kentucky-Indiana
Cleveland, Ohio
Columbus , Ohio
Dallas, Texas
Dayton, Ohio
Denver, Colorado
Detroit, Michigan
Fort Lauderdale-Hollyvood, Florida
Fort Worth, Texas
Gary-Hammond-East Chicago, Indiana
Grand Rapids, Michigan
Creensboro-Winston-Salem-High Point, North Carolina
Hartford, Connecticut
Honolulu, Hawaii
Houston, Texas
Indianapolis, Indiana
Jacksonville, Florida
Jersey City, New Jersey
Kansas City, Missouri-Kansas
Los Angeles-Long Beach, California
Louivville, Kentucky-Indiana
Memphis, Tennessee-Arkansas
Miami, Florida
Milwaukee, Wisconsin
Minneapolis- St. Paul, Minnesota
Nashville -Davidson, Tennessee
New Orleans, Louisiana
New York, New York
Newark, New Jersey
Norfolk-Portsmouth, Virginia
Oklahoma City, Oklahoma
Omaha, Nebraska-Iowa
Paterson-Clif ton-Passaic, New Jersey
Philadelphia, Pennsylvania-New Jersey
Phoenix, Arizona
Pittsburgh, Pennsylvania
Portland, Oregon-Washington
Providence-Pawtucket-Harwick, Rhode Island-
Massachusetts
Richmond, Virginia
Rochester, New York
Sacramento, California
St. Louis, Missouri-Illinois
Salt Lake City, Utah
San Antonio, Texas
San Bernadino-Rlverside-Ontario, California
San Diego, California
San Francisco-Oakland, California
San Jose, California
Seattle-Everett, Washington
Sprlngfleld-Chlcopee-Holyoke, Massachusetts-
Connecticut
Syracuse, New York
Tampa-St. Petersburg, Florida
Toledo, Ohio-Michigan
Washington, D.C. -Maryland-Virginia
Youngstown-Warren, Ohio
Adjusted
Value
0.1835
0.5836
0.2173
0.4762
0.2806
0.1392
0.0931
0.6036
0.7019
0.3056
0.0711
0.5837
0.7621
0.4585
0.1421
0.9604
-0.0248
0.582}
0.4172
0.2106
0.5527
0.2137
0.5981
0.4496
0.5573
0.4303
0.3169
-0.1694
0.8089
0.8315
0.2603
0.1198
0.7634
0.8451
0.8129
0.7218
0.1781
0.5179
0.1000
0.2507
0.8852
0.9966
0.1371
0.2234
0.7246
0.3510
1.0273
0.1606
0.1123
0.2196
0.9576
0.1583
0.5728
0.2463
0.6042
0.9020
0.8189
0.7364
1.0144
0.4634
0.6157
0.5526
0.5617
0.6848
0.3634
Standardized Scores
Rank
53
25
51
33
44
57
62
22
18
43
63
24
14
35
41
4
64
26
37
32
10
48
23
36
29
38
42
65
12
10
45
59
13
8
9
17
54
32
61
46
7
3
58
49
16
40
1
55
60
50
5
56
27
47
21
6
11
15
2
34
20
31
28
19
39
gating
E
B
D
C
D
E
E
B
B
D
E
B
B
C
0
A
E
B
C
b
C
D
B
C
C
C
D
E
A
A
D
E
B
A
A
B
E
C
E
D
A
A
E
D
B
D
A
E
E
D
A
E
B
D
B
A
A
B
A
C
B
C
C
B
D
Standardized Scores
Valuf
-0,1356
0.0786
-0.1060
0.01.28
-0.1051
-0.2305
-0.2385
0.0'>62
0.1433
-0.0930
-0.1189
-0.0252
0.1584
0.0503
•0.0591
0.3241
-0.3553
0.1572
-0.0323
-0.1965
0.0379
-0.2608
0.0352
-0.2692
0.0374
-0.1268
-0.0196
-0.5717
0.2132
0.2809
-0.1199
-0.3219
0.3227
0.1496
0.3530
0.2195
-0.1756
0.0198
-0.21204
-0.1944
0.3415
0.2747
-0.2677
-0.1554
0..1476
-0.0748
0.1981
-0.1508
-0.2498
-0.1409
0.3750
-0.1709
0.0579
-0.2018
0.1034
0.2661
0.3300
0.2354
0.3063
0.0175
0. J509
-0.0262
0.0892
0.1087
-0.1079
Hank
47
24
42
25
41
56
57
27
20
40
44
35
15
28
38
4
64
16
37
53
30
59
12
61
11
46
34
65
14
6
45
55
12
18
9
13
62
29
63
52
1
7
60
50
19
19
1
49
58
48
2
51
26
54
22
8
11
10
5
33
17
36
23
21
43
RatlnK
D
B
D
B
D
E
E
C
B
D
B
C
B
C
D
A
E
B
C
D
C
E
C
z
c
D
C
E
A
A
0
E
A
B
A
A
E
C
E
D
A
A
E
D
B
D
A
D
E
D
A
D
C
D
B
A
A
A
A
C
t
C
B
B
D
Mean (7) - 0.4809
Standard Deviation (a) - 0.2928
Mean (x) - 0.0000
Standard Deviation (s) * 0.2071
A - Outstanding (s 8 + •)
B - Excellent (I + .2B« i t f * + i)
C - Good (» - .28> < C < S » .28i)
II - Adequate (»-i)
I - lubetandarrf <**-•)
130
-------
adjusted standardized scores, -0.17 and -0.02, respectively. The
negative scores indicate that these two SMSA's had extremely high
negative input values that more than offset the positive input factors.
As a result, the overall score is negative.
The remaining 11 substandard SMSA's, though still with index values
below the mean minus one standard deviation (3? - S), do not vary much
from the adequate SMSA's. The remaining substandard SMSA's are
Cincinnati, Birmingham, Newark, Richmond, Memphis, Paterson/Clifton/
Fassaic, Baltimore, St. Louis, Providence/Pawtucket/Warwick, New Orleans,
and Akron. One finding in the social component is that the New York
SMSA, while surrounded by three "E" rated SMSA's, still received an
index of 0.52, slightly greater then the metropolitan mean value of
0.48. In the ranking, New York is the last SMSA with a value greater
than the mean, ranked 32nd among the 65 SMSA's, and rated "good" in the
social component. This is due primarily either to better opportunities
for self-support and individual development, greater equality among
individuals, better community living conditions, or a combination of the
three. For example, the individual equality index for Newark is sub-
stantially below that for New York; while New York was ranked 17th
in this category, Newark ranked 64th. Table A-5 in the Appendix gives
the following information: Negro male to total male unemployment rate
adjusted for educational differences in 1970, was 1.65 and 2.22,
respectively; meaning that Negro males in New York had an unemployment
rate 65 percent higher than the average for all males, but, in Newark
the figure was 122 percent; the Negro females in both SMSA's had a
23 percent and 61 percent higher than average unemployment rate; the
ratio of male to female unemployment rate adjusted for education in
New York was 0.81, while in Newark it was 0.63.
As far as community living conditions are concerned, New York shows
considerably higher indexes for many factors than does Jersey City.
Jersey City, though showing an average birth rate, has the second
highest death rate, next only to Tampa, with more than 12 deaths per
1,000 in 1970. Very few sports, dance, drama, or music events and
virtually no cultural institutions and fairs and festivals were held
in Jersey City in 1970. In addition, there were very few recreational
facilities. The estimated cost of living index was 124, or 24 percent
higher than the U.S. average.
131
-------
CHART 5
HANK
REGIONAL VARIATIONS IN INDEXES;
SOCIAL COMPONENT (L)
SOCIAL COMPONENT (A)
SMSA ADJUSTED STANDARIZED SCORE
X--S
A <
B <
c <
D
I Portland, Oreg - Wash
2 Seattle - Everett. Waih
3 Omaha. Nebr - Iowa
4 Denver. Colo
5 Sacramento, Calif
6 San Diego. Calif
7 Oklahoma City. Okla
8 Milwaukee. Wis
9 Minrteopolts-St.Paul, Minn
10 Lo> Angeles- Long Beach, Calif
II San Francisco-Oakland, Calif
. 12 Kama, City. Mo - K>
13 Miami. Fla
14 Columbia. Ohio
IS Son Joe. Calif
16 Phoenix. Ariz .
17 Nashville - Davidson, Tenn
18 Buffalo, NY
19 Washington. DC - Md - Vo
20 Syracuse. NY
21 Son Bermdino-ltiverside-Ontario, Calif
22 Boston, Mass
23 Hartford. Conn
24 Cleveland. Ohio
25 Albany-Schenectady-Troy, NY
26 Fort Lauderdale-Hollywoad. Fla
, 27 Salt Lake City. Utah
f 28 Toledo. Ohio - Mich
29 Houston, Texas
30 Grand Rapids, Mich
31 Tampa-St. Petersburg. Fla
32 New York, NY
33 Anaheim - Santo Ana- Garden Grove, Calif
34 Springfield-Chicopee-Holyoke, Mass-Conn
35 Dallas. Texas
36 Honolulu, Hawaii
37 Fort Worth, Texas
V. 38 Indianapolis, Ind
• 39 Youngstown - Warren, Ohio
40 Pittsburgh, Pa
41 Dayton. Ohio
42 Jacksonville, Fla
43 Chicago. Ill
44 Atlanta. Go
45 Louisville, Ky- Ind
46 Norfolk - Portsmouth, Va
47 Son Antonio, Texas
48 Greensboro-Winston-Salem-High Point, NC
49 Philadelphia, Pa - NJ
50 Rochester, NY
51 Allentown- Bethlehem- Easton, Pa-NJ
t 52 Gary - Hammond - East Chicago. Ind
' $3 Akron, Ohio
54 New Orleans. La
55 Providence - Powtucket - Warwick. Rl - Mass
56 St.Louis. Mo- III
57 Baltimore, Md
58 Peterson - Clifton - Possoic. NJ
59 Memphis. Tenn - Ark
60 Richmond, Vo
61 Newark, NJ
62 Birmingham, Ala
63 Cincinnati, Ohio - Ky - Ind
64 Detroit, Mich
65 Jersey City, NJ
x-s
5? - Mean - .4809
S = Standard Deviation «
X+S
.2928
132
-------
The weakest factors in Jersey City are individual concerns. People in
the city have very limited opportunities for development of individual
capabilities. Individual choice is restricted by immobility, lack of
information, and spatial extension. For instance, only 36.3 percent
of the population older than 25 have completed 4 years of high school or
more--some 16.0 percentage points below the U.S. level. While 82.5
percent of the households in the U.S. have one or more automobiles, the
corresponding figure for Jersey City is only 59.1 percent. Population
density in the city is extremely high, with 12,963 persons per square
mile—about 35 times the U.S. average of 360 persons. It shows, on
the average, a fairly equal state between males and females, and whites
and nonwhites. In fact, the city is one of the best in terms of racial
nondiscrimination as reflected by income and unemployment differences
adjusted for education. The extremely low positive indexes in the
factors of individual concerns and community living conditions are more
than offset by the negative indexes in the category of individual
equality. As a result, the overall index value for the city in the
social component becomes negative.
Detroit ranks low on all three counts in the social component—individual
concerns, individual equality, and community living conditions.
Nevertheless, Detroit received better than average ratings in several
social factors. For instance, it ranks 29th in promoting maximum develop-
ment of individual capabilities, 21st in racial equality, and 35th in
other social living conditions. The low positive index values in
individual concerns and community living conditions, however, are not
enough to make up for the high negative index values in the individual
equality category. For example, the SMSA had very high spatial inequal-
ities as shown by housing segregation and income inequality indexes
between city and suburban residents—the central city's population share
was 10.0 percent higher than its income share, and the percentage of
nonwhites living in the central city was 2.42 times as many as those
living in the entire metropolitan area; comparing respectively to 6.0
percent and 1.3 times in the U.S. The additive model employed in the
study, hence,derived a negative social component index for the SMSA
(-0.02). This suggests that more local emphasis might be placed on
policies aimed at reducing individual inequalities between races, sexes,
central city, and suburban populations.
Portland, Seattle/Everett, Omaha, Denver, and the other "A" rated SMSA's
rated better than the U.S. average in almost all social factors. However,
there are differences among them in terms of their strengths and
weaknesses. Portland and Seattle/Everett are very close in the social
component with indexes of 1.03 and 1.01. However, the living cost in
133
-------
the former is much lower than in the latter SMSA. People in Portland
have a lower birth rate and enjoy more recreational facilities on a
per capita basis than in Seattle/Everett but have a higher unemployment
rate and lower family income relative to Seattle/Everett.
Omaha has very good existing opportunity for self support and good
community facilities. There is an excellent equality between sexes in
the area; e.g., the male to female ratio of professional employment
adjusted for education was 1.24, meaning that given equal educational
background, males have only 24 percent more professional employment
than females in employment distribution among occupations, while in
the U.S. and Portland the corresponding figures are 49 percent and 48
percent, respectively. The higher male to female ratio in professional
employment adjusted for education may be partly attributed to sex
discrimination.
Another outstanding SMSA in the Midwest is Kansas City. It ranks
fourth in terms of facilities for good community living and has excellent
opportunities for self support and very little sex discrimination.
Racial discrimination is evidently a problem for the area since it ranked
46th in terms of individual equality between white and nonwhite popula-
tions. By contrast, the St. Louis SMSA, which is also constituted of
counties in two states, reveals a significantly lower social quality of
life than Kansas City. The substandard rating for St. Louis is primarily
due to its weak showing in the areas of individual concerns and individ-
ual equality. As far as living conditions are concerned St. Louis
ranks 31st, or average. The weakest factors in St. Louis are considered
to be spatial inequalities and the restricted opportunities for individ-
ual choice. The housing segregation index is 1.55 for St. Louis, for
example; meaning that the central city has proportionally 1.55 times more
nonwhite population than that of the metropolitan area as a whole. The
U.S. figure was only 0.2. In the central city, the young (under five)
and the old (over 65) age groups accounted for more than one-fifth of
the total population (22.7 percent), the second highest among the
large SMSA's next only to Fort Lauderdale/Hollywood. The number of
motor vehicles registered in the area is 498 per 1,000 population,
about 90 percent of the U.S. standard.
As noted earlier, the adjusted standardized scores for the larger SMSA's
range from -0.17 to 1.03. In the social component widespread distribution
among the indexes can be discerned from its coefficient of variation
which is equal to 0.61 (0.29/0.48). This coefficient of variation is
much greater than those obtained for the other components, implying that
134
-------
n
0)
01
o
X
W
ra
bo
C
C
O -~-
•iH l-J
4-1 ^
D
,£> 4J
•H C
M CU
4-J C
ra o
a
•H
o
-------
social quality of life varies appreciably. A quick glance at Figure 5,
a geographic distribution of ratings, shows that the SMSA's of the North-
east account for most of the lower ratings and the SMSA's of the West
Coast and Midwest dominate the outstanding ranks.
The rankings in this study are highly consistent with those of state
studies by Liu, Wilson, Smith, et al. Comparing the results in this
study to similar regional studies, the rankings among the metropolitan
areas agree with extremely high consistency. For instance, in his
recent study of 50 large cities Louis also rated Seattle, Portland
Denver, Minneapolis, Oklahoma City, and Omaha as the best and Newark,
St. Louis, Detroit, Baltimore, and Birmingham as the worst American
cities. Although there is no single indicator for the social component
computed in the metropolitan studies by Coughlin and Smith, they demon-
strate nearly identical patterns of geographic distribution of social
well-being.W
In summary, this section has undertaken an extensive investigation of
social well-being among the 65 large SMSA's. In attempting to identify
relative weakness and strength, numerous concerns with our social
evolvement in the urban U.S. have been examined through criteria such as
independency, equality, and community living conditions. A total of
more than 50 factors affecting our social well-being were studied and
some important implication are delineated. It is not the purpose of
this study to try to identify all weaknesses and strengths for each
SMSA with the information contained in Table A-5 in the Appendix.
However, this study does point out the fact that there are no totally
perfect or imperfect regions. In other words, the "A" rated SMSA's may
have just as many problems, though of a different nature, as those "E"
rated SMSA's.
SUMMARY AND CONCLUSION
The five quality of life components—Economic, Political, Environmental,
Health and Education, and Social--have been analyzed. The relative
27/ See Arthur M. Louis, "The Worst American City," Harpers Magazine
(January 1975), p. 71; David M. Smith, The Geography of Social
Well-Being (New York: McGraw-Hill Company, 1973), p. 109; and
Robert E. Coughlin, "Goal Attainment Levels in 101 Metropolitan
Areas" (Mimeograph, Number 41) (Philadelphia, Pennsylvania:
Regional Science Research Institute, 1970).
136
-------
weaknesses and strengths of each of the 65 large SMSA's have been
studied with more than 100 factors.
For economic well-being, it is shown that the strongest areas in this
country are concentrated in the Northeast—the manufacturing belt—and
a few young metropolitan areas such as Dallas, Fort Worth, Houston, and
Portland. The weak regions are in the South and in the New England
states. The variation in economic factors among regions tends to be
relatively smaller than other quality of life components. Different
methods of index construction have been used. The standardized scores
differ only slightly from the adjusted standardized scores—the rank
order correlation coefficient between the two sets is highly significant
and is equal to 0.96. However, the factor and component analyses produce
considerably different rankings, especially for SMSA's rated "B,"
"C," and "D" by the other two methods. Since a detailed technical
investigation on factor or component analysis is beyond the scope of
this report, the results from factor and component analysis are not
included.
The local governments in the Northeast and the West Coast are found to
be more professional and efficient and people more active in politics
than in the southern states. Although a clear visual differentiation
between the outstanding SMSA's and the substandard SMSA's was apparent
in Figure 2, the actual variations in this political component are not
appreciable. In fact, the coefficient of variation computed from the
indexes for the political component is the smallest among the five being
discussed, i.e., 0.25. This implies that the quality of political life
enjoyed by individuals among the large urban areas does not vary much.
The West Coast shows distinctly better environmental quality than the
manufacturing belt—particularly the East North Central region. Indus-
trialization and economic growth in the East North Central region have
apparently created a substandard environment in terms of air, water,
visual, noise, and solid waste pollution. The land utilization pattern
in this region is such that relatively fewer green land and recreational
areas are made available for public use, as compared to the Pacific Coast
and other regions. Variations in environmental deterioration among
regions are fairly high—the coefficient is 0.33.
The geographic distribution of the quality of health and education
varies from that of the other three components, although the Pacific Coast
region once again ranks as outstanding. The position of southern states is
even more diminished—none of the large SMSA's in the South is rated
either excellent or outstanding. The variations in health and education
137
-------
quality in the areas are high with the coefficient being 0.70, highest
among the five components under consideration. This implies that
policies related to health and educational improvement or investment
in human resources are essential and for the overall enrichment of urban
quality of life.
Th evaluation of social well-being in this country tends to favor the
Midwest and the Pacific Coast regions. The aging metropolitan areas in the
Northeast and South are rated inferior when compared to others in
social life quality as judged by individual concerns, equality, and
community living conditions. A great dispersion in this social component
was also observed geographically. The coefficient of variation for
this component is 0.61, second highest among the five coefficients
discussed. This indicates that social concerns are critical issues.
The substandard regions must go a long way to catch up with the out-
standing EMSA's, as shown by the social component. Conceivably, improve-
ments in health and education will directly enhance the social quality
of life. Policies to achieve these objectives for every American are
essential.
138
-------
CHAPTER VI
QUALITY OF LIFE FINDINGS AND IMPLICATIONS:
MEDIUM METROPOLITAN AREAS (M)
The quality of life for the 83 medium sized SMSA's with a population between
200,000 and 500,000 was studied and the results will be discussed in
this chapter. The geographic distribution of these SMSA's follows the
same pattern as the large SMSA's, clustering mostly in the eastern
regions, such as East, North and South Central, Middle and South
Atlantic. Less than one-third of the 83 SMSA's are in the states west
of the Mississippi River; of these about one-third are in the State of
California. There is no medium SMSA in many states such as Missouri, the
Dakota's, Nebraska, Montana, Wyoming, Idaho, Utah, or Maine.
Since the criteria employed to measure the quality of life in this
chapter were identical to those discussed in the last chapter, only
empirical results and their implications will be delineated.
The analyses in this chapter will follow the same format as those de-
scribed in the preceding chapter. A short summary of the overall
findings will be given in the last section after the five quality of
life components have been described.
ECONOMIC COMPONENT
The index, rank, and rating for economic quality of life of the 83
medium sized SMSA's are contained in Table 6. There are 16 SMSA's with
an economic quality of life index beyond 2.14, or the sum of mean plus
one standard deviation (x + s), and thus rated "A" or outstanding. This
group of SMSA's is led by Fort Wayne and South Bend in Indiana, and
Kalamazoo in Michigan, with indexes valued at 2.95, 2.70, and 2.54,
respectively. Following them, most economic outstanding SMSA's are
shown in the East North Central Region, especially surrounding the
Great Lakes areas. West Palm Beach, Florida, is the only one in the
South and Eugene, Oregon, the only other along the West Coast. Des
Moines, Iowa, Wichita, Kansas, and Tulsa, Oklahoma, in the Midwest also
scored "A." It is interesting to note that three "E" rated SMSA's
appeared in the West Coast--Tacoma in Washington, Fresno and Salinas/
Monterey in California. In contrast to the economic power of the
139
-------
TABLE 6
INDEX AND RATING OF ECONOMIC COMPONENT (M)
Adjusted Standardized Score
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.
80.
81.
82.
83.
84.
85.
86.
87.
88.
89.
90.
91.
92.
93.
94.
95.
96.
97.
98.
99.
100.
101.
102.
103.
104.
105.
106.
107.
108.
109.
110.
111.
112.
113.
114.
115.
116.
117.
118.
119.
120.
121.
122.
123.
124.
125.
126.
127.
128.
129.
130.
131.
132.
133.
134.
135.
136.
137.
138.
139.
140.
141.
142.
143.
144.
145.
146.
147.
148.
SMSA
Albuquerque, N. Hex.
Ann Arbor, Mich.
Appleton-Oshkosh, vis.
Augusta, Ca.-S.C.
Austin, Texas
Bakersfield, Calif.
Baton Rouge, La.
Beaumont-Port Arthur-Orange, Texas
Binghamton, N.Y.-Pa.
Bridgeport, Conn.
Canton, Ohio
Charleston, S C.
Charleston, M. Va.
Charlotte, N C.
Chattanooga, Term.-Ga.
Colorado Springs, Colo.
Columbia, S.C.
Columbus, Ga.-Ala.
Corpus Christl, Texas
Davenport-Rock Island-Holine,
Iowa-Ill.
Des Koines, Iowa
Eric, Pa.
Eugene, Oreg.
Evansville, Ind.-Ky.
Fayettevtlle, N.C.
Flint, Mich.
Fort Wayne, Tnd.
Fresno, Calif.
Greenville, S.C.
Hamilton-Middleton, Ohio
Harrlsburg, pa.
Huntington-Ashland, W. Va.-Ky.-Ohio
Huntsville, Ala.
Jackson, Miss.
Johnstown, Pa.
Kalamazoo, Mich.
KnoxvJlle, Tenn.
Lancaster, Pa.
Lansing, Mich.
Las Vegas, Nev.
Lawrence-Haverhill, Mass.-N.H.
Little Rock-North little Rock, Ark.
Lorain-Elyria, Ohio
Lowell, Mass.
Macon, Ga.
Madison, Wis.
Mobile. Ala.
Montgomery, Ala.
New Haven, Conn.
New London-Groton-Norwich, Conn.
Newport News-Hampton, Va.
Orlando, Fla.
Pensacola, Fla.
Peoria, 111.
Raleigh, N.C.
Reading, Pa.
Rockford, 111.
Saglnaw, Mich.
Salinas-Monterey, Calif.
Santa Barbara, Calif.
Santa Rosa, Calif.
Scranton, Pa.
Shreveport, La.
South Bend, Ind.
Spokane, Wash.
Stanford, Conn.
Stockton, Calif.
Tucoma, Wash.
Trenton, N.J.
Tucson, Ariz.
Tulsa, Okla.
Utica-Rome, N Y.
ValleJo-Napa, Calif.
Waterbury, Conn.
West Palm Beach, Fla.
Wichita, Kansas
Vllke»-Barre-Hazleton, Pa.
Wilmington, Del.-N. J.-Md.
Worcester, Haas.
York, Pa.
Value
1.8571
2.1429
2.4214
0.9571
1.7857
1.2643
1.4143
1.7214
1.7071
1.8071
2.1643
0.9643
1.2714
1.6643
1.3214
1.5714
1.4286
1.0786
1.9000
2.0286
2.2500
1.4000
0.9643
1.6500
2.2000
1.9143
0.6643
2.0000
2.9500
1.0214
1.5643
2.0071
1.5643
1.1643
1.6071
1.3929
1.1786
2.5429
1.7214
1.8357
2.0929
1.6786
1 . 8000
1.4000
1.9643
1.4571
0.9357
1.7857
1.1143
0.7500
2.0429
1.3357
1.3214
1.4500
1 . 3929
1.1857
2.4071
1.8214
1.6714
2.2071
2.4071
1.1857
1.6786
1.6000
1.4786
1.5071
2.7000
1.5214
2.4714
1.6071
1.1500
1.3000
1.2000
2.4429
1.2786
1.5786
2.1429
2.4786
2.1714
1.4500
1.6786
1.6643
1.9643
Mean
Hank
26
15
1
80
32
68
57
33
35
29
14
78
67
40
63
47
56
76
25
19
10
58
79
42
12
24
83
21
1
77
48
20
49
73
43
60
72
3
34
27
17
36
30
59
22
53
81
31
75
82
18
62
64
54
70
8
28
39
11
9
71
37
45
52
51
2
50
5
44
74
65
69
6
66
46
16
4
13
55
38
41
23
(x't * 1.6601
Rating
B
A
A
t
C
C
0
c
c
B
A
E
D
C
e
c.
D
^
B
B
A
0
I
C
A
B
E
B
A
e
c
B
c
E
C
D
E
A
C
B
B
C
C
D
B
D
E
C
E
E
B
D
D
D
E
A
B
C
A
A
E
C
C
D
D
A
D
A
C
E
D
D
A
D
C
A
A
A
D
C
C
B
Standardized Scores
Value
-0.0229
0.2434
0.4163
-0.4525
-0.1103
-0.4707
-0.0876
0.0059
-0.0371
0.2115
0.2695
-0.5003
-0.3004
0.0869
-0.1591
-0.0924
-0.1358
-0.5140
1.6571
0.1872
0.3633
-0.2761
-0.4831
-0.0240
0.2416
0.1100
-0.716.7
0.1574
0.6407
-0.4896
0.0788
0.1766
-0.1013
-0.4157
-0.0160
-0.2205
-0.3021
0.4534
-0.0628
0.0879
0.15(>3
0.0706
-0.0327
-0.1527
0.1077
-0.22'!7
-0.3720
-0.0969
-0.37?9
-0.5886
0.1020
-0.25%
-0.1545
-0,17')5
-0.0576
-0.3716
0.3758
0.1318
0.1040
0.1677
0.3682
-0.3016
0.0577
-0.09'!1
-0.1045
-0.1380
O.S6;'7
-0.0701
0.91M
0.8132
-0.3634
-0.25J4
-0.3673
0.4586
-0.2365
-0.09SI9
0.2968
0.4769
0.2748
-0.2535
0.01(0
0.0025
0.2058
Rank
39
16
9
76
53
77
46
36
42
IB
15
80
67
31
38
47
54
81
1
20
12
66
78
40
17
26
83
23
5
79
32
21
51
75
38
60
68
8
44
30
24
33
41
56
27
61
73
48
74
82
29
65
57
59
43
72
10
25
28
22
11
69
34
49
52
55
4
45
2
3
70
63
71
7
62
50
13
6
14
64
35
37
19
Hating
C
B
A
E
D
E
C
C
C
B
B
e
D
c
D
c
D
E
A
B
B
D
E
C
B
B
E
B
A
E
C
B
C
E
C
D
D
A
C
C
B
C
C
D
B
D
E
C
E
E
C
D
D
D
C
E
A
B
B
B
A
D
C
C
D
D
A
C
A
A
D
D
D
A
D
C
B
A
B
D
C
C
B
A • Outstanding (i X + 8)
B - Excellent (* + ,28» 5 B < S + s)
C - Good <; - .28. <: C f » + .28s)
D - Adequate («-a
-------
large SMSA's, the West Coast in general and California in particular
revealed a weaker economic status relative to other medium SMSA's in
the country. Among the 14 SMSA's with index values lower than the
mean minus one standard deviation, Fayetteville, North Carolina;
Montgomery, Alabama; Macon, Georgia; Augusta, Georgia/South Carolina;
El Paso, Texas; and Charleston, South Carolina, received the lowest
economic indexes with values below 1.00 as compared to the metropolitan
average of 1.67. Figure 6 depicts the geographic variations in economic
ratings among the 83 SMSA's.
For weakness and strength identification, Table B-l in the Appendix
provides some useful information. The results in the preceding
chapter have clearly indicated that there are neither perfect SMSA's
or SMSA's consistently ranked worse in all factors selected as criteria
in this study. Conceivably, similar results can be observed through
careful study of Table B-l in the Appendix. For instance, Fort Wayne
rated only average in community income equality and the chamber's effort
in stimulating regional economic growth. While there were 31.3 percent
of the families in the U.S. with income below the poverty level or
above $15,000 in 1970, this SMSA also had 28.6 percent, not very much
better than the U.S. average. The Chamber of Commerce in the area
employed 4.3 persons per 100,000 population, ranking only 29th. Never-
theless, this area is one of the few SMSA's with an extremely high
percentage of family income beyond the poverty level and many
owner-occupied housing units.
South Bend ranked second highest in terms of community economic health,
but when income distribution, productivity, economic concentration, etc.,
are all combined, its unemployment rate in 1970 was fairly high, 4.7
percent or 0.3 percentage points higher than the U.S. average.
Kalamazoo, as another example, ranked high in individual economic well-
being but only 16th in community economic health, and it had the same
high unemployment rate as South Bend. Furthermore, the income distri-
bution in Kalaraazoo is more unequal than in South Bend; the percentage
of families with income below poverty level or greater than $15,000
was 31.4 percent in Kalamazoo versus 25.8 percent in South Bend.
The personal income per capita in Fayetteville amounted to $2,340 or
more than one quarter below the U.S. average of $3,139, and its total
bank deposits per capita showed $576, or just about 23.1 percent of the
U.S. average of $2,492. These low values may be greatly attributed to
the low labor productivity and a high unemployment rate of 5.2 percent.
However, the inequality in income distribution in this SMSA tends to
be no problem at all. Montgomery's best points are the rankings
141
-------
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c
o
•H
M
4J
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Q ex
o o
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cx o
bO O
O C
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w
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•H
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c
•H
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C
OJ
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4J
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£• i,
1] ~ §
<5 §
V 8. "
0 J
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g
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4J
C
QJ
,— 1
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0)
o
X
H
•
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3
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142
-------
of inequality and unemployment; for in th'ese two factors, Montgomery
ranked even above the average, 33rd and 28th, respectively. In Montgomery
96.2 percent of the total labor force in the SMSA were employed in
1969, as compared to only 95.6 percent in the U.S. as a whole.
Individual economic well-being in Macon, especially the average per-
sonal income per capita, was not as severe a problem as other community
economic structures and viability, such as family poverty and -capital
funds available for investment. The undeflated income per capita in
the SMSA was $2,733, or only 87.1 percent of the national level, but
Macon ranked 50th among the 83 medium SMSA's. Partly due to unequal
distribution of income, this area had only 84.6 percent of families
with income above the poverty level or about 4.7 percentage points
below the national counterpart. Probably because of the relatively
low income per capita being partially ascribed to low labor productivity,
total bank deposits per capita in the area were relatively lower than
in other SMSA's and much lower than the national figure—only equal to
49.7 percent.
Two SMSA's in such opposite geographic locations as Tacoma and West
Palm Beach were rated substandard and outstanding, respectively.
Although West Palm Beach had almost the highest indicators in average
income per capita and individual wealth, the income and wealth distri-
bution among individuals and families in the area was fairly unequal.
In contrast, the poverty and income distribution situation in Tacoma
was about average, but the unemployment, the capital availability, and
the specialized economic structure substantially impeded the area's
community economic health. The closest SMSA to Tacoma, Eugene, with
an unemployment rate as high as 8.1 percent in 1970, still obtained
very high average income per capita and wealth status because of its
higher labor productivity. A reasonably good distribution of income
also helped advance the rating of this SMSA to the "A" category.
In passing, if should be noted that this study always evaluates the
results deduced from the adjusted standardized rather than the unadjusted
standardized scores because the extremely high value of one factor
(or a few factors) may dominate the overall component rating if it is
(they are) not adjusted. A good example was found with Stockton SMSA
in California. Without adjusting the standardized "Z" scores of all
factors, the area received an average economic index of 0.8132, or
more than two standard deviations above the mean and hence, rated out-
standing or "A." This could be the result of two extremely high "Z" scores
computed for its savings and bank deposits per capita. These two "Z"
143
-------
CHART 6
REGIONAL VARIATIONS IN INDEXES;
ECONOMIC COMPONENT (M)
HANK
SMSA
ADJUSTED STANDARDIZED SCORE
x-s
X+S
A <
B
E
1 Fort Wayne. Ind.
2 South Bend. Ind.
3 Kalamozoa. Mich.
4 West Palm Beach, Flo.
5 Stamford, Conn.
6 Tulsa. Okla.
7 Appleton - Oshkoih. Wis.
8 Peorio. III.
9 Soginow. Mich.
10 Des Moines, Iowa
11 Rockford. III.
12 Eugene, Oreg.
13 Wichita, Kans.
14 Canton. Ohio
15 Ann Arbor. Mich.
16 Waterbury, Conn.
• 17 Lansing. Mich.
18 New Haven. Conn.
19 Davenport - Rock Island - Moline. Iowa - III.
20 Hamilton - Middlcton, Ohio
21 Flint, Mich.
22 Lorain- Elyria, Ohio
23 York, Po.
24 Evonsville, Ind. - Ky.
25 Corpus Christ!. Texas
26 Albuquerque, N.Mex.
27 Lancaster, Pa.
28 Raleigh, N.C.
29 Bridgeport, Conn.
30 Lawrence- Haverhill. Moss. - N.H.
31 Madison, Wis.
32 Austin, Texas
33 Beaumont - Port Arthur - Orange, Texas
34 Knoxville, Tenn.
35 Binghomton, N.Y. - Pa.
36 Las Vegas. Nev.
37 Santa Barbara. Colif.
38 Wilmington. Del. - N.J. -Md.
39 Reading, Pa.
40 Charlotte, N.C.
41 Worcester. Man.
42 Erie, Pa.
43 Huntsville, Ala.
44 Stockton. Calif.
45 Santa Rosa, Colif.
46 Vallejo - Napo. Calif.
47 Colorado Springs, Colo.
48 Greenville. S.C.
,49 Hanisburg, Pa.
rSO Spokane. Wash.
51 Shreveport. La.
52 Scranton, Po.
53 Lowell. Mass.
54 Orlando, Flo.
55 Wilkes-Barre - Holleton, Pa.
56 Columbia, S.C.
57 Baton Rouge, La.
58 Duluth -Superior. Minn. - Wis.
59 Little Rock-North Little Rock, Ark.
60 Jackson. Miss.
61 Oxnard- Ventura, Colif.
62 New London - Groton - Norwich, Conn.
63 Chattanooga, Tenn. - Ga.
64 Newport News - Hampton, Va.
65 Trenton, N.J.
66 Utico-Rome. N.Y.
67 Charleston. W.Va.
68 Bokersfield. Colif.
W69 Tucson, Ariz.
' 70 Pensacola, Flo.
71 Salinas-Monterey, Calif.
72 Johnstown, Pa.
73 Huntington - Ashland. W.Va. - Ky. - Ohio
74 Tacomo. Wash.
75 Mobile. Ala.
76 Columbus. Go.-Ala.
77 Fresno, Calif.
78 Charleston, S.C.
79 El Paso, Texas
80 Augusta, Ga. -S.C.
81 Macon, Ga.
82 Montgomery, Ala.
83 Fayetteville. N.C.
144
X-S X
X» Mean-1.6691
$ « Standard Deviation - 0.4695
-------
scores jointly advanced the overall component rating significantly
above those for other SMSA's in the same group. With the adjusted
"Z" score method, the SMSA received the maximum grade of "5" points
for these two factors which were weighted equally with other factors to
derive the overall index. As a result of this adjustment, Stockton
received an overall index value of only 1.61 or slightly below the
group mean and hence, rated "good" rather than "outstanding."
The regional variations in indexes are shown in Chart 6. Although
there are 30 SMSA's with indexes valued outside the range of the mean
plus and minus one standard deviation, the overall variation in the
indexes is small. The coefficient of variation is equal to 0.28
(0.47/1.67). In other words, the remaining 53 SMSA's in this group
did not seem to have economic weaknesses and strengths significantly
different from each other as far as the overall results are concerned.
In addition, the distribution of the indexes for all SMSA's is very
symmetrical and tends to approach normal.
POLITICAL COMPONENT
The East North Central Region has been quantitatively identified as
the dominating region in economic viability and vitality when compared
to other regions in the preceding section. In terms of political per-
formance and government efficiency, the outstanding positions of the
metropolitan areas in the region are once again retained. As shown in
Table 7, the region accounts for more than one-half of the "A" rated
SMSA's in the political component of the quality of life measures,
i.e., 10 out of 19. Led by Duluth/Superior (Minnesota and Wisconsin)
with an index as high as 3.73, Appleton/Oshkosh, Wisconsin—3.65,
Kalamazoo, Michigan--3.51, and Madison, Wisconsin--3.51 in the East
North Central, the remaining outstanding SMSA's are Eugene and Santa
Barbara in the West Coast; Binghamton, New York/Pennsylvania; Waterbury,
Connecticut; Fort Wayne, Indiana; Bridgeport, Connecticut; Des Moines,
Iowa; South Bend, Indiana; Lansing, Michigan; Evansville, Indiana/Kentucky;
Charleston, West Virginia; and Utica/Rome, New York.
On the other end of the scale,' 15 SMSA's have been classified as sub-
standard due to their low indexes relative to other medium sized SMSA's.
Corpus Christi, Texas; Macon, Georgia; Columbia, South Carolina;
Fayetteville, North Carolina; Columbus, Georgia/Alabama; and Charleston,
South Carolina have index values substantially below the mean (2.62) minus
one standard deviation (0.60). The remaining 11 SMSA's with index values
lower than the threshhold level are also found in the southern states.
145
-------
TABLE 7
INDEX AND RATING OF POLITICAL COMPONENT (M)
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.
81,
82.
83.
85.
86.
87.
88.
89.
90.
91.
92.
93.
94.
95.
96.
97.
98.
99.
100.
101.
102.
103.
104.
105.
106.
107.
108.
109.
110.
111.
112.
113.
114.
115.
116.
117.
118.
119.
120.
121.
122.
123.
124.
125.
126.
127.
128.
129.
130.
131.
132.
133.
134.
135.
136.
137.
138.
139.
140.
141.
142.
143.
144.
145
146.
147.
148.
SMSA
Albuquerque, N. Hex.
Ann Arbor, Mich.
Appleton-Oahkosh, Ula.
Augusta, Ga.-S.C.
Austin, Texas
Bakersfleld, Calif.
Baton Rouge, La.
BrldRoport, Conn.
Canton, Ohio
Charleston, S.C.
Charleston, V Va.
Charlotte, N.C.
Colorado Springs, Colo,
Columbia, S.C.
Columbus, Ga.-Ala.
Davenport-Rock Island-Moline,
lows-Ill.
DCS Koines, Iowa
Duluth-Superior, Minn.-Wls.
El Paso, Texas
Erie, Pa.
Eugene, Greg.
Evanavllle, Ind.-Ky.
Fayettevllle, N.C.
Flint, Mich.
Fort Wayne, Ind.
Fresno, Calif.
Greenville, S.C.
Hamilton-Mlddleton, Ohio
Harrlaburg, Pa.
Huntington-Ashland, W. Va.-Ky.-Ohio
Huntsville, Ala.
Jackaon, Miss.
Johnstown, Pa,
Kalamazoo, Mich.
Knoxville, Tenn,
Lancaster, Pa,
Lansing, Mich.
Las Vegas, Nev.
Lawrence-Haverhill, Mass.-N.H.
Little Rock-NortS Little Rock, Ark.
Lorain-Elyria, Ohio
Lowell, Mass.
Macon, Ga.
Madison, Wls.
Mobile, Ala.
Montgomery, Ala.
New Haven, Conn.
New London-Groton-Norwich, Conn.
Newport News-Hampton, Va.
Orlando, Fla.
Oxnard-Ventura, Calif.
Penaacola, Fla.
Peorla, 111.
Raleigh, N.C.
Reading, Pa,
Rockford, 111.
Ssginaw, Mich.
Salinas-Montefey, Calif.
Santa Barbara, Calif.
Santa Rosa, Calif.
Scranton, Pa.
Shreveport, La.
South Bend, Ind.
Spokane, Wash.
Stamford, Conn.
Stockton, Calif.
Taeoma, Wash.
Trenton, N.J.
Tucson, Ariz.
Tulsa, Ckl«.
Utlca-Rome. N.Y.
ValleJo-Napa, Calif.
Waterbury, Conn.
West Palm Beach, Fla.
Wichita, Kanaas
Wilmington, Del.-N.J.-Md.
Worceater, Maaa.
York, Pa.
Adluated
Value
3.1111
2.5764
3.6528
2.1111
2.3125
3.1667
2.3958
2.0833
3.4375
3.3681
2.7708
1.6458
3.2431
1.9028
2 3889
2,3333
1.5764
1.6319
1 5000
2.6528
3.3333
3.7292
1.6944
2.8681
3.5000
3.2500
1.6042
3.2917
3.3750
3.0000
1.6944
2.3542
2.4514
2.4931
2.1042
1.6944
2.9375
3.5069
2.4236
2.1806
3.3194
2 . 3403
3.1319
1.7917
2.4792
2.9653
1.5417
3.5069
1.7708
1.9722
3.3056
2.8264
2.0347
2.4722
2.8611
2.0000
2.6528
2.4306
2.3958
2.5972
2.7222
2.0694
3.4444
3.3194
3.0625
1.9514
3.3264
3.0694
2.9097
2.8542
2.2014
2.7500
2.3264
2.6736
3.2222
2.6111
3.3889
2.3542
3.0764
2. 7431
2.8472
3.0000
2.0903
Stand.rdixtd Scores
Hank
22
43
2
63
60
20
52
66
7
10
36
78
18
72
54
58
81
79
83
41
11
1
75
31
5
17
80
16
9
26
76
55
49
46
64
77
29
3
51
62
13
57
21
73
47
28
82
4
74
70
15
35
68
48
32
69
42
50
53
44
39
67
6
14
25
71
12
24
30
33
61
37
59
40
19
43
8
56
23
38
34
27
65
Rating
B
C
A
D
D
B
D
D
A
A
C
E
A
E
D
D
E
E
£
C
A
A
E
B
A
A
E
A
A
B
E
D
D
C
D
E
B
A
D
D
A
D
B
E
C
B
E
A
E
E
A
B
D
C
B
E
C
D
D
C
C
D
A
A
B
E
A
B
B
B
D
C
D
C
A
C
A
D
B
C
B
B
D
Stand
Value
0.2638
0.1228
0.7234
-0.4225
-0.1483
0.5918
-0.2327
-0.3298
0.3914
0.3952
0.0703
-0.6511
0.4658
-0.4413
-0.0622
0.0520
-0.7922
-0.9817
-0.6822
0.0300
0.7497
0.6525
-0.7156
0.0646
0.6345
0.4985
-1.1716
0.4065
0.8428
0.3926
-0.7254
-0.2152
0.1380
-0.1040
-0.3161
-0.6072
0.1981
0.4462
-0.1924
-0.4401
0.4341
-0.2344
0.3292
-0.3820
-0.2189
0.2967
-0.5813
0.5680
-0.6627
-0.4259
0.3642
-0.0846
-0.4127
-0.0115
0.1108
-0.3618
-0.0370
-0.1591
-0.1803
0.0113
0.1657
-0.3327
0.3996
0.7994
0.2082
-0.4279
0.4805
0.3940
0.1175
0.3668
-0.1372
0.0463
-0.1961
0.0362
0.3138
-0.0283
0.3518
-0.1428
0.2199
0,0469
0.2218
0.1927
-0.3568
liSi
26
34
4
69
53
7
60
63
19
16
37
76
11
73
48
39
81
82
78
43
3
5
79
38
6
9
83
14
1
18
80
58
33
50
62
75
30
12
56
72
13
61
23
67
59
25
74
8
77
70
21
49
68
45
36
66
47
54
55
44
32
64
15
2
29
71
10
17
35
20
51
41
57
42
24
46
22
52
28
40
27
31
65
Rating
B
B
A
D
D
A
D
D
B
B
C
E
A
E
C
C
E
E
E
C
A
A
C
C
A
A
E
B-
A
B
E
D
B
C
D
E
9
A
D
E
A
D
B
D
D
B
E
A
E
D
B
C
D
C
C
D
C
D
P
C
B
D
p
A
D
A
B
C
B
D
C
D
C
B
C
D
B
C
B
B
D
A - Outatandlng (i x + B)
B - Excellent (S + .28s s B < « + a)
C • Cood (x - .28s f C f « + ,28s)
D • Ad.qu.t. (x-.cDSx- .281)
B • Substandard (£ x - a)
Mean (x) - 2.6236
Standard Deviation (a) - 0.5970
Mean (x) • 0.0000
Standard Deviation (s) • 0.4323
146
-------
The geographic distribution of ratings in this component as portrayed by
Figure 7 reveals a vivid, contrasting picture between the East North Central,
the West Coast, and the southern states. The dividing line in this
medium metropolitan area section is even clearer than that observed in
the large metropolitan areas.
Studies tend to associate substantially affluence with governmental
efficiency in that public expenditures are conventional measures of
government performance, and a higher level of per capita expenditure
has to come from a higher level of per capita revenue, which in turn
depends on the affluence and wealth status of the community due to the
characteristics of local tax structure. When comparing Figure 7 to
Figure 6, this cause-effect relationship is upheld also for most
metropolitan areas except those in the State of California. Economically
speaking, none of the medium SMSA's in California was rated either
outstanding(A) or excellent (B) as noted earlier, a surprising contrast
to the large SMSA's in that state. However, almost all the medium
SMSA's in the state were rated "A" or "B" in the quality of public
administration and individual political participation.
Naturally, each SMSA has its weaknesses and strengths. SMSA's could
not be rated either outstanding or substandard simply because of one
or two typical factors since the standardized scores had been adjusted
before the weighted component indexes were constructed. However, a
combination of some of the 21 factors which made up the composite
indexes for the political component would affect the rating. Duluth/
Superior, though ranked first among the 83 SMSA's in the political
component, did not have the best of all factors. In fact, the
professionalism of its local governments in 1970 was only about
average and Duluth/Superior ranked 34th in that category; nor did it
have the best informed citizenry, and the rank for that category was
about 20th in standardized "Z" scores. To be more specific, in terms
of professionalism this SMSA showed lower than U.S. average monthly
earnings for school teachers ($656 versus $682), and lower than average
police protection services. The ratio of police protection employment
per 1,000 population was 1.4 versus 2.5 in the U.S. Although by factors
reflecting individual political activities, this SMSA had a much better
than national average record. Its local Sunday newspaper circulation
of 820 per 1,000 population and the percentage of occupied housing with
television sets (96.0 percent), for example, was below that for some
other SMSA's.
147
-------
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148
-------
Appleton/Oshkosh demonstrated as one of the areas in which people
received the best welfare assistance and the area with the best govern-
mental performance, in that it had the lowest violent crime rate of
50.8 per 100,000 population (versus 397.7 per 100,000 in the U.S.)
and a very low property crime rate. A high percentage of governmental
revenues from the Federal Government (11.0 percent versus 2.7 percent
for the entire U.S.) was observed in 1970» On the other hand, the
people in Appleton/Oshkosh did not seem to be very interested in
participating in political activities and were relatively less informed
by local radio broadcasting; for instance, the percentage of presidential
votes cast among the voting age population in 1968 was 63.4 percent,
and the number of local radio stations per 1,000 population in 1970 was
0.72. Although these two figures are much higher than the U.S. counter-
parts, they are lower than those in many other SMSA's in the medium
size group (see Table B-2 in the Appendix).
Although in terms of salaries paid to policemen and firemen, etc.,
local governments in the Kalamazoo SMSA employed staff members with
outstanding professional quality; and in terms of numbers of govern-
mental employees per 1,000 people as well as in salaries paid to teachers,
the performance of the local governments judging by the observed crime
rates, the community education, and health indicators did not conform
to a high quality of professionalism. The violent crime rate in the
area as released by the FBI records in 1970 was 567.9 per 100,000 and
the property crime rate was 3,006.7 per 100,000. They were, respectively,
43.0 percent and 23.6 percent higher than the national average.
The aforementioned weaknesses of the three highest ranking SMSA's
resulted from a rudimentary investigation among the 21 politi-
cal factors selected for this study. In a like manner, the exercise
can be carried out for the SMSA's whose political quality of life
ratings are substandard.
For example, the most serious impediment for a good quality of political
life component in Corpus Christi seems to be the lack of high quality
and sufficient numbers of employees in local governments to provide
essential public services, such as education, police and fire protection,
etc. The average monthly earnings of teachers in Corpus Christi
amounted to $562, equivalent to 82.4 percent of the U.S. standard. For
every 1,000 people in Corpus Christi, there were only 1.3 policemen to
protect safety and security. Probably due to this low level of protec-
tion--48.0 percent below the U.S. standard—the violent and property
crime rates in the area were considerably higher than the U.S. average--
about 16.5 percent and 37.7 percent, respectively, in 1970.
149
-------
CHART 7
REGIONAL VARIATIONS IN INDEXES;
POLITICAL COMPONENT (M)
HANK
SMSA
ADJUSTED STANDARDIZED SCORE
X-S
X+S
B
1 Duluth - Superior. Minn. - Wis.
2 Appleton - Oshkosh, Wis.
3 Kolomauo. Mich.
4 Madison, Wis.
5 Eugene, Oreg.
6 Santa Barbara. Calif.
7 Binghomton. N.Y. - Pa.
8 Woterbury. Conn.
9 Fort Wayne. Ind.
10 Bridgeport. Conn.
II DesMaines. Iowa
12 South Bend. Ind.
13 Lansing, Mich.
14 Santo Rosa, Calif.
IS New Haven. Conn.
16 Flint, Mich.
17 Evansville. Ind. - Ky.
18 Charleston, W.Vo.
V.19 Utico-Rome, N.Y.
' 20 Bokenfield. Calif.
21 Lawrence - Hoverhill. Mais. - N.H.
22 Albuquerque, N.Mex.
23 Wichita. Kons.
24 Spokane. Wash.
25 Scranton. Pa.
26 Fresno. Calif.
27 Worcester, Moss.
28 Lowell, Moss.
29 Johnstown, Pa.
30 Stamford. Conn.
31 Erie. Pa.
32 Oxnard - Ventura. Calif.
33 Stockton. Calif.
34 Wilmington, Del. - N.J. - Md.
V 35 New London - Groron - Norwich, Conn
36 Canton, Ohio
37 Trenton, N.J.
38 Wilkes-Borre - Hazleton, Pa.
39 Sooninow. Mich.
40 Turn. Okie.
41 Davenport - Rock Island - Maline. Iowa - III.
42 Peorio, III.
43 Vallefo - Nopa, Calif.
44 Rockford, III.
45 Ann Arbor, Mich.
46 Huntington - Ashland. W.Va. - Ky. - Ohio
47 Loroln-El/ria. Ohio
V.48 Orlando, Fla.
49 Horriiburg. Pa.
SO Raleigh, N.C.
51 Knoxville. Tern.
52 Baton Kouge. La.
S3 Reading, Pa.
54 Chattanooga. Tenn. - Go.
55 Hamilton - Middleton. Ohio
56 West Palm Beach, Fla.
57 Las Vegas, Nev.
58 Colorado Springs. Cole.
59 Tucson, Ariz.
60 Austin, Texas
61 Tacoma, Wash.
62 Lancaster. Pa.
63 Augusta. Go. -S.C.
64 Huntsville, Ala.
65 York, Pa.
66 Beaumont - Port Arthur - Orange, Texas
67 Salinas - Monterey, Calif.
_ 68 Newport News - Hampton, Va.
»» 69 Peraocolo. Fla.
70 Montgomery, Ala.
71 Shreveport, La.
72 Charlotte. N.C.
73 Little Rock - North Little Rock, Ark.
74 Mobile, Ala.
75 El Paso, Texas
76 Greenville. S.C.
77 Jackson. Miss.
78 Charleston, S.C.
79 Columbus, Go. - Ala.
80 Foyerteville. N.C.
81 Columbia, S.C.
82 Maean. Go.
83 Corpus Christ!, Texas
X-S
X+S
150
K-Mean-2.6236
S - Standard Deviation - 0.5970
-------
In view of the informed citizenry in 1970, Columbia, South Carolina,
compared favorably to other SMSA's and ranked 30th in the group.
Nevertheless, its low indicators of individual participation in politi-
cal activities and local governmental factors in professionalism,
performance, and welfare assistance significantly weakened its competi-
tive situation. From the standpoint of local government performance,
Columbus, Georgia/Alabama was rated much better than average wjLth
a rank of 32nd in the group. The weak spots in the area as seen through
individual participation, welfare assistance, and professionalism are
such that Columbus ranked last as compared to the other 82 SMSA's.
As Charts 1 and 8 display, although the composite indexes for the
political quality of life among the 83 SMSA's give a relatively
larger standard deviation, the political component shows thicker and
more equal bars than the economic component. This is because the
variations in the composite indexes in the former component are not as
large as those in the latter. The coefficient of variation for the
political component is 22.8 percent whereas the economic component
is 28.1 percent. In other words, despite the relative ratings or ranks
among; the SMSA's the differences in political factors among regions are
relatively smaller than those of economic factors and much smaller than
environmental, health and education, and social factors to be discussed
in the following sections. In addition, the variations in political
quality of life indicators in the medium sized SMSA's are also smaller
than those in the large sized SMSA's. All this implies that the degree
of homogeneity from the viewpoint of political considerations is not
only higher among medium SMSA's than among large SMSA's but also
higher than other four quality of life components within the medium
size SMSA group.
ENVIRONMENTAL COMPONENT
Pollution and environmental damages have been increasingly attacked by
opponents to ec9nomic growth and industrialization. Economists have
aptly used pollution as an illustration of externalities. "The discharge
of pollutants into the atmosphere imposes, on some members of society,
costs which are inadequately imputed to the sources of the pollution by
free markets, resulting in more pollution than would be desirable from
the point of view of society as a whole,'—' explains Professor Mills
\J Edwin S. Mills, "Economic Incentives in Air Pollution Control,"
Economics of Air Pollution. Harold Wolzin (ed.), New York:
W. W. Norton & Company, Inc. (1966).
151
-------
regarding the failures of our free market mechanism when dealing with
social benefits and social costs in production involving external dis-
economies. The trade-off between economic activities and environmental
deterioration, or the degradative changes in our ecosystems, have been
thoroughly discussed by Commoner under the "Aquatic System" and the
"productive activities" of human progress.—' Quantitative measures of
pollution and other environmental changes are made available by Tobin
and others as previously described. This section presents some infor-
mation as to where in the U.S. the trade-offs or damages have occurred.
This study of environmental quality in medium SMSA's supports the
findings in the previous chapter that the Pacific region stands at
the top of the listing. All the SMSA's in the Pacific region are rated
either "outstanding" or "excellent." In fact, California has five
outstanding SMSA's, or about 40.0 percent of the total of 13 rated "A."
The five are Fresno, Salinas/Monterey, Santa Barbara, Oxnard/Ventura,
and Bakersfield. However, the best of "A" rated SMSA's is Tacoma, which
obtained an environmental quality index appreciably greater than others,
i.e., -0.07 or about three standard deviations above the mean of -0.97.
In short, this SMSA was found to have very few ecological damages or
problems (see Table 8).
Las Vegas ranks fourth and Corpus Christi, the lowest ranked SMSA in
the political component, ranks fifth in environmental quality evaluation.
The other "A" rated SMSA's are Duluth/Superior, Davenport/Rock Island/
Moline, Newport News/Hampton, Trenton, and Eugene.
Tulsa, one of the best SMSA's in economic well-being, received the lowest
environmental rating among the 83 SMSA's, with an index value of -1.62
or about 2.2 standard deviations below the mean. This resulted primarily
from its extremely high level of total suspended particulates, high noise
measures, and bad climatological data. Jointly, these factors deteriorated
its environmental quality and more than offset the relatively good recrea-
tional areas and facilities, and the low volume of solid waste and visual
pollution.
Huntington/Ashland, a metropolitan area comprised of counties in the States
of West Virginia, Kentucky, and Ohio, has the second lowest index, -1.58.
2/ Barry Commoner, "The Environment Costs of Economic Growth," Economics
of the Environment, Robert and Nancy Dorfman (eds.)(New York:
W. W. Norton & Company, Inc., 1972).
152
-------
TABLE 8
INDEX AND RATING OF ENVIRONMENTAL COMPONENT (M)
66.
67.
68.
69.
70.
71.
72.
74.
75.
76.
77.
78.
79.
80.
81
82.
83.
84
85.
86.
87.
88.
89.
90.
91.
92.
93.
94.
95.
96.
97.
98.
99
100.
101.
102.
103.
104
105.
106.
107.
108.
109.
110.
111.
112.
113.
114.
115.
116.
117.
118.
119.
120.
121.
122.
123.
124.
125.
126.
127.
129
130.
131.
132.
133.
134.
135.
136.
137.
138
139.
140.
141.
142.
143.
144
145.
146.
147
148.
SMSA
Albuquerque, N. Hex.
Ann Arbor, Mich.
AppUton-Oshkosh, Wls.
Augusta, Ca.-S C
Austin, Texas
Bakersfleld, Calif.
Baton Rouge, La.
Blnghamton, N.Y -Pa.
Bridgeport, Conn.
Canton, Ohio
Charleston, B.C.
Charleston, W. Va.
Charlotte, N.C.
Chattanooga, Tenn.-Ga.
Columbia, B.C.
Columbus, Ga.-Ala.
Davenport-Rock Island-Moline,
Iowa-Ill.
Des Holnes, Iowa
Duluth-Superior, Mtnn.-Wis.
El Paso, Texas
Erie, Pa.
Eugene, Oreg.
Evansville, Ind,-Ky.
Fayetteville, N.C.
Flint. Mich.
Fort Wayne, Ind.
Fresno, Calif.
Greenville, S.C.
Hamllton-Middleton, Ohio
Harrisburg, Pa.
Huntsville, Ala.
Jackson, Miss.
Johnstown, Pa.
Kalamazoo, Mich.
Lancaster, Pa.
Lansing, Mich.
Las Vegas, Nev.
Lawrence-Haverhill, Mass.-N.H
Lorain-Elyria, Ohio
Lowell, Mass.
Macon, Ga.
Madison, Wis.
Mobile, Ala.
Montgomery, Ala.
New Haven, Conn.
New Londort-Groton-Norwich, Conn.
Newport News-Hampton, Va.
Orlando, Fla.
Oxnard-Ventura, Calif.
Pensacola, Fla.
Peoria, 111.
Raleigh, N.C.
Reading, Pa.
Rockford, 111.
Saginaw, Mich.
Salinas-Monterey, Calif.
anca Barbara, Calif.
Scranton, Pa.
Shreveport, La.
South Bend, Ind.
Spokane, Wash.
Stamford, Conn.
Stockton, Calif.
Tacoraa, Wash.
Trenton, N.J,
Tulsa, Okla.
Utica-Rome, N Y.
ValleJo-Napa, Calif.
Waterbury, Conn.
West Palm Beach, Fla.
Wilkes-Barre-Hazleton, Pa.
Wilmington, Del . -N. J. -Md.
York, Pa.
Value
-1.2750
-0.9083
-0.9417
-1.0583
-1.0583
-0.6167
-1.0583
-0 9583
-1.0583
-0.8083
-1.1917
-1.2417
-1.3000
-1.3917
-1.0917
-1.1333
-1.4750
-1.2250
-0.3917
-0.6000
-0.9583
-0.5333
-1.0417
-0.8917
-0.5833
-0.9750
-1.0417
-1.0083
-0.9417
-0.2833
-1.1917
-0.8500
-0.8583
-1 5750
-1.2000
-1.0917
-1.2083
-0.8583
-0 7583
-1.0250
-0.9417
-0.3417
-0.6833
-I . 1917
-1.1750
-0.8833
-1.2250
-0.9083
-1.4917
-1.2500
-0.8750
-0.8750
-0.6417
-1.1083
-0.6000
-1.2250
-1.0750
-1.1750
-1.1500
-0.7000
-0.9250
-0.3000
-0.5667
-0. 8833
-1.3083
-1.4083
-1.0417
-1.0167
-0.7083
-0.8750
-0.0667
-0.6583
-0.8833
-1.6250
-0.9417
-0.8500
-0.7833
-1.3583
-1.2333
-0.7917
- 0 . 9000
-1.1833
R.nk
74
M
36
50
53
11
51
40
52
20
63
72
75
78
56
58
80
68
5
9
41
6
47
31
8
42
48
43
37
2
65
21
23
82
66
55
67
24
17
45
38
4
14
64
60
28
69
33
81
73
25
26
12
57
10
70
54
61
59
15
35
3
7
29
76
79
49
44
16
27
1
13
30
83
39
22
18
77
46
71
19
32
62
— _rt CjTrtrt
Rating
E
C
C
D
D
A
D
C
D
B
D
D
E
E
D
D
E
D
A
A
C
A
C
C
A
C
C
C
C
A
D
B
B
g
D
D
D
B
C
C
A
B
D
D
B
D
C
E
D
B
B
A
D
A
D
D
D
D
B
C
A
A
B
E
E
C
C
B
B
A
A
B
E
C
B
B
E
C
D
B
C
D
Star
Value
-0.1555
0.0132
0.0084
-0.0526
-0.1699
0.1790
-0.0693
0.0475
-0.0468
0.0534
-0.1611
-0.2596
-0.5169
-0.3757
-0.0435
-0. 1617
-0.3548
-0.1422
0.3369
0.1606
0.0393
0.1521
0.0092
0.0587
0.5195
0.0016
-0.1100
0.0049
0.0362
1.3020
-0.2653
0.0727
0.0473
-0 4829
-0.1164
-0.1084
-0.1983
0.0702
-0.0536
0.0400
1.3295
0.0394
-0.1382
-0.0154
-0.2078
-0.1347
0.0697
-0.5558
-0.2608
0.0867
0.1098
0.3127
-0.1824
0.2631
-0.1294
0.0077
-0.1111
-0.1195
0.1481
0.0048
0.5942
0.5458
0.0642
-0.1868
-0.2529
-0.0474
-0.3464
0.2165
0.0943
0.5236
0.0413
0.1343
-0.5032
0.0466
-0.0413
0.0291
-0.5097
-0.0957
-0.2154
0.1532
-0.0541
-0.1206
Ua.n IZ
idardlzed
Rank
63
36
38
46
63
11
51
27
46
26
64
73
82
78
45
65
77
62
7
12
33
14
37
25
6
42
54
40
34
2
75
21
28
79
56
53
69
22
49
31
1
32
61
43
70
60
23
83
74
20
17
8
67
9
59
39
55
57
15
41
3
4
24
68
72
47
76
10
19
5
30
16
80
29
44
35
81
52
71
13
50
58
* _ n IWY
Scores
Ratlin
D
C
C
C
D
B
C
C
C
C
D
D
E
E
C
D
E
D
A
B
C
B
C
C
A
C
D
C
C
A
D
C
C
D
D
D
C
C
C
A
C
D
C
D
D
C
E
0
B
B
A
D
B
D
C
D
D
B
C
A
A
C
D
D
C
E
B
B
A
C
E
C
C
C
E
B
D
B
C
D
n
A - Outstanding (i * + a)
B •= Excellent (x + .283 < B < x + *)
C * Good (x - .2fls < C < x + .28s)
D - Adequate
-------
This SMSA had a very minor solid waste problem generated by the manufac-
turing industry in 1970, but its water pollution was among the worst,
with an index as high as 9.26. The water pollution index was developed
on the basis of prevalence, duration, and intensity of pollution (PDI).
The original PDI index was such that a higher rank number indicates a less
urgent pollution problem. In order to be consistent with other pollution
indicators used in this study, the original PDI rank was divided into the
median PDI rank of all metropolitan areas and converted into another
index, meaning the higher the value, the more urgent the problem of
water pollution. While most medium SMSA's had water pollution indexes
ranging from 0.59 (Bakersfield) to 2.71 (Evansville), Huntington/Ashland
had an index of about 16 times as high as the best areas in California.
In addition, this SMSA also suffered from bad climatological data. For
example, it was among several SMSA's with very high mean annual inversion
frequency (42.5 percent) and very low possible annual sunshine day
(48 days).
Mobile, Alabama,and Columbia, South Carolina, are the next two SMSA's with
indexes slightly higher than Tulsa and Huntington/Ashland. While water
pollution and the lack of a relatively good natural environment are
detrimental problems in Mobile, it compared favorably to others in noise
pollution—virtually no indication of serious noise problems created by
motorcycles or a densely populated central city, etc. Columbia had environ-
mental problems quite similar to those of Mobile; in fact, the noise
pollution in Columbia was slightly better than in Mobile, but the
visual pollution and solid wastes are relatively worse.
Although Tacoma ranked first in environmental quality evaluation, this
SMSA still had some air pollution and solid waste problems. Its mean
level for total suspended particulates in 1970 was relatively high,
93.9 microgram per cubic meter, and its mean level for sulfur dioxide was
73.0 microgram per cubic meter, or 13.0 microgram per cubic meter higher
than the secondary standard level specified by the U.S. Environmental
Protection Agency. The solid waste generated in Tacoma by manufacturing
industries totaled 645.4 tons per million dollars of value added, a
relatively high figure compared to other SMSA's (see Table B-3 in the
Appendix).
The most serious problem in Fresno was the noise pollution--it had a
fairly high number of motorcycles and motor vehicles registered per 1,000
people and a relatively high population density in its central city. It
should be noted that the three factors selected to measure noise pollu-
tion need not be even the second best indicators at all since noise
154
-------
CHART 8
REGIONAL VARIATIONS IN INDEXES:. ENVIRONMENTAL COMPONENT (M)
RANK
SMSA
ADJUSTED STANDARDIZED SCORE
j?
K-S
5C+S
A.
B-
C_
D <
E <
i
2
3
4
5
6
7
9
10
II
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
..30
' 31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
. 49
' 50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
. 73
' 74
75
76
77
78
79
80
81
82
83
Tocomo. Wash.
Fresno. Calif.
Salinas - Monterey. Calif.
Los Vega), Nev.
Corpus Christi. Texas
Duluth - Superior. Minn. - Wit.
Santo 8arboia. Calif.
Eugene. Oreg
Davfnporl - Rock Island - Moline. Iowa - III.
Oxnard - Venture. Calif.
Bokersfield. Calif.
Newport News - Harrpton. Vo.
Trenton. N.J.
Lawrence - Haverhill. MOM. - N.H
Rockford. III.
Stamford. Conn
Knoxville. Tenn.
Woterbury, Conn.
Wilmington. Del. - N.J. - Md.
Bridgeport. Conn.
Hamilton - Middlelon. Ohio
Volleio - Nopa. Calif.
Horrisburg. Po.
Kalamozoo, MicK.
New Haven. Conn.
New London - Groton - Norwich, Conn.
Stockton. Calif.
Lowell. Mass
Santa Kola. Colif.
Tucson, Ariz.
Erie. Pa.
Worcester. Mass.
Madison. Wis.
Ann Arbor. Mich
Soginow, Mich.
Appleton - Oshkosh. Wis.
Fort Wayne. Ind.
Lansing, Mich.
Utica- Rome. N.Y.
Beaumont - Port Arthur - Orange, Texas
Des Moines, Iowa
Evansville, Ind. - Ky.
Flint, Mich.
Spokane, Wash.
Lancaster, Pa.
Wichita, Kara.
El Paso, Texas
Foyetteville. N.C
South Bend, Ind.
Augusta, Go. - S.C.
Baton Rouge, La.
Binghomton, N.Y. -Pa.
Austin, Texas
Peoria, III.
Jackson, Miss.
Chattanooga, Tenn - Go.
Orlando, Flo.
Colorado Springs, Colo.
Reading, Pa.
Loroin - Elyria. Ohio
Raleigh. N.C..
York. Po.
Canton, Ohio
Little Rock - North Little Rock, Ark.
Greenville, S.C.
Huntsville. Ala.
Johnstown, Pa.
Columbus, Ga. - Ala.
Mocon, Ga.
Pensocola, Flo.
Wilkes-Barre - Hazleton. Pa
Charleston, S.C.
Montgomery. Ala.
Albuquerque, N.Mex.
Charleston, W.Vo.
Scranton. Pa.
West Palm Beach. Fla.
Charlotte. N.C.
Shreveport, La.
Columbia. S.C.
Mobile. Ala.
Hunlington - Ashland. W.Va. - Ky. - Ohio
Tulu. Oklo.
X-S X
X-Mean--0.9700
S * Standard Deviation
'0.2963
155
-------
created is a function of the age and the frequency of vehicle use, not
necessarily the number that are registered. However, the lack of any
other better indicators and comparable statistical data on noise mea-
sures for all SMSA's necessitates the adoption of the present measures.
Similar to Fresno, the neighboring SMSA--Salinas/Monterey--had some noise
problem. In addition, its visual pollution was worse than the average--
2.9 percent of the single housing units in the area were dilapidated in
1970.
Las Vegas, the last "A" rated SMSA with an index value two standard
deviations above the mean, benefits significantly from the natural
environmental measures. Furthermore, there was virtually no air and
visual pollution. The noise problem in that area was found intolerable.
The number of motorcycles registered in Las Vegas was the second highest
among the 83 SMSA's, next only to BakersfieId, 36.0 per 1,000 people as
compared to 43.0 per 1,000. Las Vegas also had 698 motor vehicles
registered per 1,000 people, the third highest in the group of medium
SMSA's. It is interesting to note that noise, as other disamenities,
has been shown not only to have direct, adverse effects on human life,
but also indirect, adverse effects on human life, and also indirect,
o /
adverse effect on property values, etc.—'
Tacoma ranks as an SMSA with outstanding environmental quality, but sub-
standard economic health. Tulsa was revealed to be an opposite case,
where some trade-off between industrial development and economic growth
and the environmental quality occurred. Another case similar to Tulsa
was found in West Palm Beach. Nevertheless, the third typical case was
observed in Eugene, where both economic and environmental quality was
outstanding in 1970. The trade-off hypothesis between industrial growth
and environmental deterioration seems to be less significant in the
medium size metropolitan areas than in the large areas. Comparison of
Figure 7 to Figure 8 is still quite convincing that the hypothesis is
plausible, particularly when references are made for the SMSA's surrounding
the Great Lakes area.
The standard deviation among indexes in the environmental component is
the smallest among the five quality of life components in this size
group, i.e., 0.30. It means that the dispersion of the indexes are the
smallest and they are clustered around the mean. This can be easily
3/ For instance, see Jean-Francois Gautrin, "An Evaluation of the Im-
pact of Aircraft Noise on Property Values," Land Economics, Vol. 51,
No. 1 (February 1975), pp. 80-85.
156
-------
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157
-------
discerned from Chart 8, which is very narrow in shape. The actual
variations among the values of indexes, however, does tend to be rela-
tively high. The coefficient of variation is equal to 30.5 percent,
slightly higher than the two components discussed previously. What this
means is, the geographic differences in environmental quality among
SMSA's tend to be slightly higher than those in political and economic
factors. This higher variation, obviously, can be partially attributed
to the variations in natural environment in general, and the climatolog-
ical data in particular.
HEALTH AND EDUCATION COMPONENT
The composite indexes for the health and education component contained in
Table 9 show a wide dispersion of the index values. Indeed, this com-
ponent has the highest standard deviation among the five quality of life
components, i.e., 0.67. This wide dispersion of indexes can also be
visualized from the lowest of -0.19 for Greenville, South Carolina, to
2.92 for Madison, Wisconsin. In other words, the quality level of
health and education as measured by this study varies significantly among
the SMSA's.
In addition to Madison, there are a dozen more SMSA's that are outstanding
in health and education quality of life measures. They are: Ann Arbor
and Lansing, Santa Barbara and Salinas/Monterey, Stamford, Eugene,
Albuquerque, Tucson, Binghamton, Appleton/Oshkosh, Wichita, and Des Moines.
The distribution of these "A" rated SMSA's and the excellent, or "B"
rated SMSA's, tend to favor the West Coast and the East North Central
regions. As shown in Figure 9, no substandard SMSA is found west of a
line drawn through Mobile and Montgomery, Chattanooga, Huntington/Ashland,
and Wilkes-Barre/Hazelton. In other words, the substandard regions in
this quality of life component are geographically more typical than
are the other quality of life components. The other nine "E" rated
SMSA's east of the line are Macon and Columbus, Charleston, Reading, York,
Augusta, Scranton, Fayetteville, and Greenville.
The large variations in index values and the clustered geographical distri-
bution of outstanding and substandard ratings should be analyzed separately
with the various health and education factors chosen for this study, since
it is obvious that the "A" rated SMSA's, just as "E" rated SMSA's, have
problems as well as prides of different natures and in varying degrees.
The index for Madison exceeds the mean by 2.7 times the standard devia-
tion and ranks extremely outstanding (see Chart 9). This region shows
158
-------
TABLE 9
INDEX AND RATING OF HEALTH AND EDUCATION COMPONENT (M)
SMSA
66. Albuquerque, N. Hex.
67. Ann Arbor, Mich.
68. Appleton-Oshkosh, Vis.
69. Augusta, Ga.-S C.
70. Austin, Texas
71. Bakersfleld, Calif.
72. Baton Rouge, La.
73. Beaumont-Port Arthur-Orange, Texas
74. Binghamton, N Y.-Pa.
75. Bridgeport. Conn.
76. Canton, Ohio
77. Charleston, S,C.
78. Charleston, W. Va.
79. Charlotte, N C.
80. Chattanooga, Tenn.-Ca.
81. Colorado Springs, Colo.
82. Columbia, S.C.
83. Columbus, Ga.-Ala.
84. Corpus chrlsti, Texas
85. Davenport-Rock Island-Moline,
Iowa-Ill.
86. DCS Moines, Iowa
87. Duluth-Superior, Minn.-Wls.
88. El Paso, Texas
89. Erie, Pa.
90. Eugene, Oreg.
91. Evansvllle, Ind.-Ky.
92. Fayettevllle, N.C.
93. Flint, Mich.
94. Fort Wayne, Ind.
95. Fresno, Calif.
96. Greenville, S.C.
97. Hamilton-Middleton, Ohio
98. Harrisburg, Pa.
99. Huntington-Ashland, W. Va.-Ky.-Ohlo
100. Huntovllle, Ala.
101. Jackson, Miss.
102. Johnstown, Pa.
103. Kalamazoo, Mich.
104. Knoxvllle, Tenn.
106. Lansing, Mich.
107. Las Vegas, Nev.
108. laurence-Haverhlll, Mass.-N.H.
110. Loraln-Elyria, Ohio
111. Lowell, Mass.
112. Macon, Ga.
113. Madison, Wis.
114. Mobile, Ala.
115. Montgomery, Ala.
116. New Haven, Conn.
117. New London-Groton-Norwich, Conn.
118. Newport News-Hampton, Va.
119. Orlando, Fla.
121. Pensaeola, Fla.
122. Peoria. 111.
123. Raleigh, N.C.
124. Reading, Pa.
125. Rockford, 111.
126. Saginau, Mich.
128. Santa Barbara, Calif.
129. Santa Rosa, Calif.
130. Scranton, Pa.
132. South Bend. Ind.
133. Spokane, Wash.
134. Stamford, Conn.
135. Stockton, Calif.
136. Tacoma, Wash.
137. Trenton, N J.
138. Tucson, Ariz.
139. Tulsa, Okla.
140. Utlca-Rome, N.Y
141. VBlleJo-Napa, Calif.
142. Waterbury, Conn.
143. West Palm Beach, Fla.
144. Wichita, Kans.
146. Wilmington, Del.-N. J.-Md.
147. Worcester, Haas.
148. York, Pa.
A - Outstanding (z x -f a)
Value
2.2000
2.4250
1.8625
0.3250
1.7250
0.9250
1.7250
0. 9000
1.9375
1.4625
0.6500
0.0875
0.6500
1.1125
0.1750
1.4750
0.5875
0.1000
0.8000
0.5000
1.7750
1.5375
1.2875
1.0125
2.2875
0.7375
0.3625
1.1250
1.3000
1.4500
-0.1875
1.1500
0.9875
0.0750
1.2500
0.8500
0.5125
1.6375
0.9750
2.4250
0.8250
1.3750
0 7750
0.7000
1.3750
0.0625
2.9250
0.0250
-0.0250
1.4625
0.8250
0.5625
0.5375
1.7125
0.5500
0.7500
1.4375
0.2750
0.8125
0.7750
2.0750
2.3750
1.4000
0.3250
1.1375
1.5875
2.3500
1.2625
0.8000
0.9375
2.1750
1.2750
1.2625
1.3750
0.7125
0.6875
1.8250
0. 2125
1.1000
0.9125
0.3125
Standard De
Rank
7
2
11
72
14
44
15
46
10
21
61
78
62
38
76
20
63
77
52
69
13
19
30
40
6
57
70
37
29
23
83
35
41
79
34
48
68
17
42
64
3
49
26
54
59
27
80
1
81
82
22
50
65
67
66
56
24
74
51
55
4
25
71
36
18
5
32
53
43
8
31
33
28
58
60
12
75
39
45
73
(x) • 1.0779
vtatlon (s) - 0
Rating
A
A
A
E
B
C
B
C
A
B
D
E
D
C
E
B
D
E
D
D
A
B
B
C
A
D
E
C
B
B
E
C
C
E
C
D
D
B
C
A
D
B
D
D
B
E
A
E
E
B
D
D
D
D
D
B
E
D
D
A
B
E
C
B
A
C
B
C
A
B
C
B
D
D
A
E
C
C
E
6727
Value
0.7270
1.7834
0.7346
-0.5948
0.5820
0.1912
0.3598
-0.1707
0.5664
0.2134
-0.2771
-0.6201
-0.2364
-0.0124
-0.5614
0.2375
-0.3255
-0.7272
-0.1791
-0.4070
0.4405
0.2950
0.1266
-0.0711
0.9609
-0.2224
-0.6854
0.1683
0.0689
0.2980
-0.8821
0.0028
-0.0832
-0.5970
0.1349
-0.2080
-0.6673
0.4193
-0.1180
-0 4906
0.7987
-0.2330
0.1578
-0. 1790
-0.2156
0.1334
-0.6359
1.5937
-0.7247
-0.9653
0.2660
-0.2598
-0.2261
-0.3040
0 4873
-0.2885
-0.1558
0.2234
-0.5928
-0.2111
-0.1744
0.8828
0.1771
-0.7011
-0.0078
0.3045
1.1208
0.2983
-0.1839
-0.0845
0.5653
0.0387
-0.0214
0.1164
-0.1649
-0.3617
0.3958
-0. 7857
-0.0133
-0.0849
-0.5498
Standard D
Bank
8
1
7
72
9
26
17
49
10
25
62
74
60
38
70
23
65
80
52
67
14
21
32
41
4
57
77
28
34
20
82
36
42
73
30
54
76
15
45
6
59
29
51
56
31
75
2
79
83
22
61
58
64
63
47
24
71
55
50
5
27
78
37
18
3
19
53
43
11
35
40
33
48
66
16
61
39
44
69
evlation (B) -
»'""«
A
A
A
E
A
B
B
D
A
B
D
E
D
C
E
B
D
E
D
D
B
B
C
C
A
D
E
B
C
B
e
C
C
E
C
D
E
B
C
A
D
B
n
D
C
E
A
E
E
B
D
D
E
C
D
A
B
E
C
C
B
A
B
D
C
A
C
C
C
D
D
B
E
C
C
E
0.5198
C - Good (5 - .28s < C < x + ,28s)
D • Adequate (x - s < D < x - .28.)
E - Substandard (<; x - •)
159
-------
CO
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160
-------
RANK
A<
B'
C i
D '
E i
' i
2
3
4
5
6
7
8
9
10
II
12
k. 13
r M
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
. 3'
f 32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
' 47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
.69
' 70
71
72
73
74
75
76
77
78
79
80
81
82
83
CHART 9
REGIONAL VARIATIONS IN INDEXES:
HEALTH AND EDUCATION COMPONENT (M)
SMSA ADJUSTED STANDARDIZED SCORE
Modiion, Wli.
Ann Arbor, Mich.
Loming, Mich.
Santo Barbara, Calif.
Stamford, Conn.
Eugene, Oreg.
Albuquerque, N.Mex.
Tucson. Aril.
Salinas - Monterey. Calif.
Bingriomton. N.Y. - Pa.
Appleton - Oshkoih, Wi>.
Wichita, Kam.
Des Moines, Iowa
Austin, Texas
Baton Rouge, La.
Oxnord - Ventura, Colif.
Kolamazoo, Mich.
Spokane. Wash.
Duluth - Superior, Minn. - Wis.
Colorado Springs, Colo.
Bridgeport, Conn.
New Haven, Conn.
Frejno, Colif.
Raleigh, N.C.
Santo Rosa, Calif.
Lawrence- Hoverhill, Mass. - N.H.
Lowell, Moss.
Valle|o - Napa. Calif.
Fort Wayne, Ind.
El Paso, Texas
Tulso, Okla.
Stockton, Calif.
Utica- Rome, N.Y.
Huntsville, Alo.
Hamilton - Middleton, Ohio
South Bend, Ind.
Flint, Mich.
Charlotte, N.C.
Wilmington, Del. - N.J. - Md.
Erie, Pa.
Harrisburg, Pa.
Knoxville, Tenn.
Trenton, N.J.
Bokersfield, Colif.
Worcester, Mass.
Beaumont - Port Arthur - Orange, Texas
Shreveport, La.
Jackson, Miss.
Las Vegas, Nev.
New London - Groton - Norwich, Conn.
Rockford, III.
Corpus Christ!, Texas
Tacomo, Wash.
Little Rock - North Little Rock, Ark.
Saginaw. Mich.
Peoria, III.
Evansville, Ind. - Ky.
Waterbury, Conn.
Lorain - Elyrio. Ohio
West Palm Beach, Flo.
Canton, Ohio
Charleston, W.Va.
Columbia, S.C.
Lancaster, Pa.
Newport News - Hampton, Va.
Pensocola, Flo.
Orlando, Fla.
Johnstown, Pa.
Davenport - Rock Island - Moline, Iowa - III.
Fayetteville. N.C.
Scranton, Pa.
Augusta, Ga.
York, Pa.
Reading, Pa.
Wilkes-Barre - Hazletan, Pa.
Chattanooga, Tenn. - Ga.
Columbus, Ga. - Ala.
Charleston, S.C.
Huntinaton - Ashland, W.Va. - Ky. - Ohio
Macon, Go.
Mobile. Ala.
Montgomery, Ala.
Greenville, S.C.
5t-S
S-s
X'+s
161
X -Mean - 1,0779
S - Standard Deviation - 0.6727
-------
the best factors of health and education as compared to the rest of the
82 SMSA's in the medium sized group. For example, Madison had the lowest
infant mortality rate in 1970, 14.2 per 1,000 live births or only two-
thirds the U.S. average. A very low death rate was observed, 6.9 per
1,000 or about 30.0 percent below the U.S. death rate. As far as
educational attainment is concerned, next only to Colorado Springs and
Santa Barbara, Madison had the highest percentage of persons 25 years
and above who had completed 4 years of high school or more, 71.2 percent
versus 52.3 percent in the U.S. In 1970, there were more than 15 of
every 100 males between 16 and 21 years of age who were not high school
graduates in the U.S. The corresponding figure for Madison was only 5.
The percentage of persons 25 years old or over who had completed 4 years
of college or more in this SMSA was more than twice that for the country
as a whole. The number of physicians available for every 100,000 popu-
lation in the region "in 1970 was about 2.4 times the U.S. level. Such
comparisons can be carried out for the remaining health and education
variables employed in this study.
Next to Madison, Ann Arbor, Lansing, and Santa Barbara are the top ranking
SMSA's in the health and education component. They all are characterized
by having a large state university in the region, and conceivably, the
health and education evaluations tend to favor these SMSA's. This in-
stitutional effect undoubtedly contributed to a certain degree to the high
ratings for other outstanding SMSA's/. Ann Arbor ranked third in individual
educational attainment and first in all community health and education
conditions; however, its 1970 infant mortality rate was a bit higher
than the U.S. average. Although individual health and education conditions
in Lansing were outstanding, its medical manpower and facility availability
did not score comparably with the numbers of dentists and physicians, and
the hospital beds per 100,000 was slightly below the U.S. average. In
the same manner, Santa Barbara's index value was lowered by the average
medical care availability in its community.
At the other end of the bar chart, Chart 9, one can easily observe a
larger number of SMSA's with low indexes, but relatively fewer differences
among them than those among the excellent and outstanding SMSA's.
Greenville, the lowest SMSA, showed its weakest points in individual
education. The median school years completed in this region among the
population 25 years old and over was reported to be 10.9, the lowest
level of educational attainment among the 83 SMSA's--1.2 years below
the U.S. level. The other SMSA that has a negative index is Montgomery.
In contrast to Greenville, Montgomery had better educational conditions,
but worse health conditions. The infant mortality rate in Montgomery was
162
-------
the highest. It was the only SMSA with more than 30 deaths per 1,000
live births in 19.70, a record of 10 deaths more than the U.S. average.
Mobile is one of the few SMSA's consistently rated substandard in the
quality of life components. It ranks 81st in this component with a
positive index, meaning that its negative factors were at the aggregate
level still more than offset by positive factors. Its index is 0.03,
or about 1.6 standard deviations below the mean. The weakest point of
this region was in its education; individual as well as community efforts
in human investment tend to be far behind the national standard. In
1970, for every 100 persons 25 years of age or over, about 42 persons had
completed 4 years of high school and seven persons had finished college, 10
and 3 persons fewer than the U.S. counterparts, respectively. The health
situation and the medical care provision in the region were not much
better than educational attainment. The infant mortality rate in the
area outnumbered the U.S. by 1.6 deaths more per 1,000 live births for
every 100,000 population; the region was served only by about 103
physicians, 50 physicians short of the U.S. level. Its per capita local
government expenditure on health in 1970 was more than one-third below
the national average.
The health and education indexes for the medium SMSA's displayed not only
a large standard deviation but also a very high coefficient of variation,
i.e., the r = 0.62. In comparison, the indexes for this medium size
group are ultimately less heterogeneous than those for the large SMSA's
in which the coefficient of variation was computed at 0.70. If the large
variation in indexes is interpreted to denote the differential health and
education quality among U.S. urban areas, the coefficient of variation
indicates that the problem of health and education inequality in the
medium SMSA's was relatively less serious than in the large SMSA's.
SOCIAL COMPONENT
The social quality of life among the medium SMSA's as measured by this
study tends to confirm the findings from the study for the large SMSA's
in that most outstanding SMSA's are in the regions west of the Mississippi
River. While most of the substandard large SMSA's are scattered through-
out the Middle Atlantic, East, North and South Central, the substandard
medium SMSA's are clustered in the South Atlantic and East South Central
regions. In addition, this study found that the social quality of life
measures are even more highly associated with the health and education
quality of life measures in the medium than in the large group of SMSA's.
163
-------
Table 10 shows that Des Moines, one of the outstanding regions in economic,
political, and health and education components, scores first in the social
component with an index of 1.32, or about 2.4 standard deviations above
the mean score of 0.49. With indexes slightly below Des Moines, Eugene
ranked second and Madison third. The list of outstanding medium SMSA's
in social quality of life includes 10 more SMSA's--Wichita, Spokane,
Appleton/Oshkosh, Duluth/Superior, Ann Arbor, Santa Barbara, Worcester,
Tacoma, Colorado Springs, and Fort Wayne.
Among individual concerns that people in Des Moines tend to enjoy most
are widening opportunity for individual choice with high mobility, better
information, and spatial extension. As far as community living conditions
are concerned, Des Moines is one of the best SMSA's. The residents'
social quality of life is enriched outstandingly by the availability of
various facilities such as banking, shopping, recreational, etc. However,
the area is by no means the perfect place for providing all types of
social quality of life. As revealed by this study, it has a critical
problem in racial inequality. It ranked 72nd among the 83 SMSA's when
income, unemployment rate, and professional employment ratios between
nonwhite and total population adjusted for educational attainment were
compared to other areas. As an example, the ratio of Negro to total
population median family income adjusted for education in 1970 was 0.71,
meaning that Negro median family income was only 71.0 percent of the
average median family income in Des Moines. This ratio was seven percentage
points below the U.S. level. The ratio of professional employment in
Des Moines between the populations, adjusted for educational difference,
was only 43 percent of the U.S. average.
Eugene is one of the few SMSA's whose ratings have been consistently out-
standing in all quality of life components as disclosed by this study.
This fact, however, does not imply that Eugene had all the best ratings
either. On a relative basis, Eugene, though ranked high in many sub-
component categories, showed only average rankings in community general
living conditions and the facilities category. For instance, only 91
percent of occupied housing had telephones available; its cost of living
is about the same as the U.S. average; its number of selected service
establishments per 1,000 people was .slightly below the corresponding
national figure.
Except for the economic component, Madison stands exceptionally high in
all the quality of life components. The strong points of this region
in the social component categories are demonstrated by the highest over-
all rating in the individual concerns such as the existing opportunities
for self-support, for individual capability development, and for
164
-------
TABLE 10
INDEX AND RATING OF SOCIAL COMPONENT (M)
66.
67.
68.
69
70
71.
72.
73
74.
75.
76.
77
78.
79.
80
81.
82.
83
84.
85
86.
87
88.
89.
90.
91.
92
93.
94.
95.
96.
97.
98.
99
100.
101.
102.
103.
104.
105.
106.
107.
108.
109.
110.
111.
112.
113.
114.
U5
116.
117.
118.
119.
120
121.
122
123.
124.
125
126.
127.
128.
129.
130.
131.
132.
133.
134.
135
136.
137.
138
139
140.
141
142.
143.
144
145.
146.
147.
148.
SMSA
Albuquerque, Mew Mexico
Ann Arbor, Michigan
Appleton-Oshkosh, Wisconsin
Augusta, Georgia-South Carolina
Austin, Texas
Bakersfleld. California
Baton Rouge, Louisiana
Beaumont-Port Arthur-Orange, Texas
Binghamton, New York-Pennsylvania
Bridgeport, Connecticut
Canton, Ohio
Charleston, South Carolina
Charleston, West Virginia
Charlotte, North Carolina
Chattanooga, Tennessee-Georgia
Colorado Springs, Colorado
Columbia, South Carolina
Columbus, Georgia-Alabama
Corpus Christ!, Texas
Davenport-Rock Island-Hollne, lova-llllnois
Des Moines, Iowa
Duluth-Superlor, Minnesota-Wisconsin
El Paso, Texas
Erie, Pennsylvania
Eugene, Oregon
Evansville, Indiana-Kentucky
Fayettevllle, North Carolina
Flint, Michigan
Fort Wayne, Indiana
Fresno, California
Greenville, South Carolina
Hamilton-Middleton, Ohio
Harrisburg, Pennsylvania
Huntington-Ashland, West VIrglnla-Kentucky-Ohio
huntsville, Alabama
Jackson, Mississippi
Johnstown, Pennsylvania
Kalamazoo, Michigan
Knoxville, Tennessee
Lancaster, Pennsylvania
Lansing. Michigan
Las Vegas, Nevada
Lawrence-Haverhill, Massachusetts-New Hampshire
Little Rock-North Little Rock, Arkansas
Lorain-Elyria, Ohio
Lowell, Massachusetts
Macon, Georgia
Madison, Wisconsin
Mobile, Alabama
Montgomery, Alabama
New Haven, Connecticut
New London-Groton-Norwich, Connecticut
Newport News-Hampton, Virginia
Orlando, Florida -
Oxnard-Ventura, California
Pensacola, Florida
Peoria, Illinois
Raleigh, North Carolina
Reading, Pennsylvania
Rockford, Illinois
Saginaw, Michigan
Salinas-Monterey, California
Santa Barbara, California
Santa Rosa, California
Scranton, Pennsylvania
South Bend, Indiana
Spokane, Washington
Stamford, Connecticut
Stockton, California
Tacoma, Washington
Trenton, New Jersey
Tucson, Arizona
Tulsa, Oklahoma
Utlca-Rome, New York
Vallejo-Napa, California
Waterbury, Connecticut
West Palm Beach, Florida
Wichita, Kansas
WlIkes-flarre-HazeIton, Pennsylvania
Wilmington, Delaware-New Jersey-Maryland
Worcester, Massachusetts
York, Pennsylvania
Value
0.4704
1.0205
1.1075
0.0539
0.7041
0.2502
0.5199
0 4404
0 6848
0.5826
0.3160
-0.1268
0.3726
0 5993
0.0014
0.8953
0.0657
-0.0701
0.4818
0.5864
1.3197
1.0333
0.4601
0.5385
1.2617
0.4387
0.0068
0.5172
0.8673
0.6579
0.1535
0.2516
0.4825
0 0780
-0.1253
0.0691
0.3667
0.8011
0.2258
0.1355
0.7408
0.8404
0.6545
0.3733
0.3523
0.5119
0.0200
1.2014
-0.2661
-0.1114
0.6692
0.5058
0.3679
0.3552
0 4437
0.0217
0.5174
0.3074
0.2705
0.5126
0.3535
0.6651
0 9701
0.7239
0.5358
0.1250
0.6098
1.1078
0.8212
0.6136
0.9543
0.3168
0.5731
0.5416
0.4485
0.6496
0.4734
0.7189
1.1741
0.1482
0.3135
0.9578
0.1015
Rank
45
8
6
74
20
64
36
49
21
31
59
82
52
29
78
12
73
79
43
30
1
7
46
34
2
50
77
38
13
24
66
63
42
71
81
72
54
16
65
68
17
14
25
51
57
40
76
3
83
80
22
41
53
55
48
75
37
61
62
39
56
23
9
18
35
69
28
5
15
27
11
58
32
33
47
26
44
19
4
67
60
10
70
Rating
C
A
A
1!
B
D
C
C
B
C
D
E
D
B
E
A
E
E
C
C
A
A
C
C
A
C
E
C
A
B
D
D
C
E
E
E
D
B
D
E
B
B
B
D
D
C
E
A
E
E
B
C
D
D
C
E
C
D
D
C
D
B
A
B
C
E
B
A
B
B
A
D
C
C
C
B
C
B
A
D
D
A
E
Standardized Scores
Mean 00 - 0.4901
Standard Deviation (s) - 0.3515
Value
-0 0181
0.5311
0 7624
-0 2847
0.1916
-0 0600
0 0323
-0.0226
0.1510
0.0525
-0.0701
-0 4419
-0 0659
0 0868
-0.2815
0 2563
-0 2128
-0.4957
-0 0659
0.0692
0.5766
0.3995
-0.1044
-0.0119
0.4985
-0.0405
-0.4507
0.0783
0.3737
0.1576
-0.2711
-0 1148
-0.1000
-0 2647
-0 5550
-0.3256
-0.2445
0.1704
-0.1041
-0.4222
0.2114
0.3666
0.1185
-0.0310
-0.0581
-0.0644
-0.2693
0 6336
-0.4815
-0.4270
0.1780
0.0436
•0.1581
-0.0858
0.0194
-0.2663
0.0153
-0 0683
-0 1838
0 0270
-0.0340
0 1692
0 3355
0.1435
0 0253
-0.4327
0 0656
0.3848
0.2928
0.0326
0.3254
-0.1745
0.0990
-0.0237
-0.0186
0 0741
-0.0343
0.0259
0.3856
-0.2575
-0 2145
0.3264
-0.3749
Mean
;andard Dev
Rank
41
4
1
73
17
50
34
43
22
jl
55
79
53
26
72
15
64
82
52
29
3
6
59
40
5
48
80
27
9
21
71
60
57
68
83
74
66
19
58
76
16
10
24
45
49
51
70
2
81
7/
18
32
61
56
38
69
39
54
63
35
46
20
11
23
37
78
30
8
14
33
13
62
25
44
42
28
47
36
7
67
65
12
75
- 0
lation (
Rating
C
A
A
E
H
C
C
C
B
C
,,
b
C
ti
K
B
(1
E
C
L
A
A
U
C
A
L
E
b
A
B
0
[)
I)
D
E
E
D
B
0
E
B
A
b
C
C
C
n
A
E
'
B
C
D
D
C
D
C
C
D
C
C
B
A
B
C
E
C
A
A
C
A
D
B
C
C
C
C
C
A
0
D
A
E
.0000
s) - 0.2744
A - Outstanding (i i + •>
C - Good (ft - .78§ < C
.28«)
D - Adequate (5-«
-------
individual choices. It also displayed very good community living
conditions with a very low percentage of people working outside the
county of residence (3.4 percent versus 17.8 for the U.S.); very little
problem in housing segregation and central city-suburban sprawl; lots
of sports, cultural and recreational activities. The weaker points in
the region are some racial discrimination and some unpleasant factors
in general living conditions, such as the national equivalent crime rate
and living costs.
After assessing more than 50 factors which influence our social quality
of life, this study derived a lowest social component index of -0.27
for Mobile. This means that the combined positive factors affecting
social quality of life in that region are outweighed by the negative
factors. In contrast to Eugene, Mobile is one of the few regions whose
quality of life ratings have consistently fallen into the substandard
category. The low index for Mobile in the social component resulted
from its low ratings in individual concerns, especially in promoting
maximum development of individual capability such as investment efforts
in education and vocational training by individuals and government,
and the lack of opportunities for self-support or for becoming inde-
pendent. For example, the labor force participation rate was very
low, 61.8 percent. And among those a high percentage was unemployed.
A relatively high percentage of married couples was found without
their own households, and yet a very high percentage of children under
18 were not living with both of their parents (22.8 percent in Mobile
versus 17.3 percent in the U.S.). Per capita local government expen-
diture for education was $94, or $52 short of the U.S. norm in 1970.
Only a small percentage of both males and females in the area between
16 and 64 who completed less than 15 years of school had vocational
training. The negative sign for Mobile's index was derived from the
high value of negative factors in individual inequalitjr between races,
sexes, and central city and suburban. Other negative factors such as
percent of occupied housing with one or more persons per room (12.0
percent against"8.2 percent in the U.S.) and a high birth rate also
partly contributed to the negative index.
A negative index is also found in Charleston, Huntsville, Montgomery,
and Columbus (Georgia/Alabama). Except for Huntsville, all of these
low ranking SMSA's have been mentioned at least three times as being
substandard. Although they have average or good environmental quality,
they compared unfavorably to other medium SMSA's economically, politi-
cally, and socially. The most critical reason for their consistent low
rating is probably due to the relatively low educational attainment
166
-------
CHART 10
REGIONAL VARIATIONS IN INDEXES:
SOCIAL COMPONENT (M)
RANK
SMSA
ADJUSTED STANDARIZED SCORE
X-S
A<
B<
I Del Moines. Iowa
2 Eugene. Oreg
3 Madison. Wit
4 Wichito. Kam
5 Spokane. Wash
6 Appleton . Oshkoth. Wil
7 Dululh - Superior. Minn - Wil
8 Ann Arbor. Mich
9 Santo Barbara. Calif
10 Worthier. Mais
11 Toco™. Woih
12 Colorado Springs, Colo
, 13 Fort Wayne. Ind
M Lai Vegai. Nev
15 Stamford. Conn
16 rsolamazoo. Mich
17 Laming. Mich
18 Santa Roto. Calif
19 West Palm Beach. Fla
20 Austin. Texas
21 Binghomton, NY - Pa
22 New Haven, Conn
23 Salinas - Monterey. Calif
24 Fresno. Calif
25 Lawrence - Hoverhill. Mass - NH
26 Vallejo- Napa, Calif
27 Stockton, Calif
28 South Bend, Ind
V.29 Charlotte. NC
30 Davenport - Rock Island - Moline, Iowa - III
31 Bridgeport, Conn
32 Tucion. Ariz
33 Tulsa. Oklo
34 Erie, Po
35 Scranton, Pa
36 Baton Rouge. La
37 Peoria, III
38 Flint, Mich
39 Rockford, III
40 Lowell. Mass
41 New London - Cretan - Norwich, Conn
42 Harrisburg, Po
43 Corpus Christ!, Texas
44 Waterbury, Conn
45 Albuquerque, NMex
46 El Poso. Texas
47 Ulico - Rome. NY
48 Oxnord- Ventura, Calif
49 Beaumont-Port Arthur-Orange, Texas
V.50 Evansville. Ind- Ky
51 Little Rock- North Little Rock, Ark
52 Charleston. WVo
53 Newport News - Hampton. Va
54 Johnstown. Po
55 Orlando, Fla
56 Saginaw, Mich
57 Loroin-Elyria, Ohio
58 Trenton. NJ
59 Ctinlon, Ohio
60 Wilmington, Del-NJ-Md
61 Raleigh, NC
62 Reading. Pa
63 Hamilton- Middleton. Ohio
64 Bokersfield, Calif
65 Knoxville, Tenn.
66 Greenville, SC
67 Wilkei-Barre-Hoileton. Fa
68 Lancaster, Pa
69 Shreveport, La
70 York. Pa
71 Huntington-Ashlond. WVo-Ky-Ohio
72 Jackson, Miss
73 Columbia. SC
74 Augusta. Go - SC
75 Pensocola, Fla
76 Mocon. Go
77 Fayetteville. NC
76 Chattanooga. Tenn - Go
79 Columbus, Go - Ala
80 Montgomery, Ala
81 Huntiville. Ala
82 Charleston. SC
83 Mobile, Ala
S-S X
S - Mean - .4901
X+S
S - Standard Deviation > .3515
167
-------
0)
o
W
ra
Ml
C
C
O
4J C
03
-------
and lower quality of physical health among the residents. The educa-
tional and health policies directed at solving these areas' problems
would seem to be not only desirable but also more efficient than other
policies.
The number of SMSA's identified by this study to have substandard social
quality of life totaled 16. In addition to the five SMSA's with
negative indexes, the remaining 11 are Chattanooga, Fayetteville, Macon,
Pensacola, Augusta, Columbia, Jackson, Huntington/Ashland, York,
Shreveport, and Lancaster. As Charts 9 and 10 illustrate, there exists
an extremely strong correlation between SMSA's rated substandard in
both the health and education component and the social component. For
the East South Central and the South Atlantic regions, this strong
correlation is observed even for the four quality of life components
except environmental. As pointed out previously, economic, political,
health and education, and social quality of life are interdependent.
Neither the education and health nor the political factors can fully
explain the low ratings of the social component in the South. However,
economic weakness in the South can be considered as the probable basic
cause for the strong correlations among the low quality of life ratings
for the SMSA's.
The standard deviation which has been used to show the range of index
values is found to be relatively small for the social component, equal
only to 0.35,because many negative quality of life factors were in-
cluded in the component. As a result, the bar chart, Chart 10, looks
much narrower than the others, such as health and education for
example. In terms of variation among index values, it is the coefficient
of variation that matters. The coefficient of variation for the social
component for medium SMSA's is extremely high, i.e., 0.71. Specifically,
this high coefficient indicates that people in the medium SMSA's had
substantially differing levels of quality of life in 1970. Indeed, the
varying quality of life experienced by them is less equal in social
concerns than in any others.
SUMMARY AND CONCLUSIONS
Among the medium SMSA's, the preceding sections have illustrated
different quality of life patterns as compared to those measured for the
large SMSA's. Economically, the most viable and wealthy SMSA's are
concentrated in the East North Central Region. The Pacific region
is found to be relatively weaker than the Midwest and the Middle Atlantic
regions. This is in contrast to the economic powers that the large
SMSA's displayed in the Pacific region. However, the only SMSA in
169
-------
the State of Oregon, Eugene, was still rated outstanding. The South
Atlantic Region showed little economic strength; the only exception
being West Palm Beach, the only outstandingly wealthy SMSA in the
South. The quality variation of economic well-being over regions is
not appreciably large, however; the coefficient of variation among
the composite economic indexes is 0.28 percent, even smaller than that
for the large SMSA's.
The highest political quality of life is found in the States of Michi-
gan, Indiana, Wisconsin, Connecticut, California, and New York, while
the local governments in the South tend to be incompetent and less
efficient in the provision of public goods and services. Despite the
fact that the SMSA's in this group are geographically drastically
differentiated by political component ratings, the actual index vari-
ations within the 83 SMSA's are the smallest among the five quality
of life components, with a coefficient of 0.24 percent. This is
similar to the findings in the large SMSA group. In short, political
quality of life in the country tends to be closer than in the other
components.
The Pacific region once again is identified as enjoying the best
environmental quality. Except for a few SMSA's, the East North Central
Region reveals some support for the trade-off hypothesis between
economic growth and environmental damages since most SMSA's in the
region were rated only "adequate." The coastal SMSA's in New England
and Middle Atlantic regions are classified as excellent. There are
only about 10 substandard SMSA's scattered through the East and South
of the United States. The environmental deterioration and the quality
variation in the medium sized SMSA's as measured do not seem to be
appreciable since the coefficient of variation of the indexes is only
about 0.30.
The health and education component measures indicate the best quality
areas are in the Pacific and the East North Central regions, though
they are mixed with "good" and "adequate" SMSA's. The SMSA's in the
Midwest are also recognized as outstanding and excellent. The "E"
rated SMSA's are found in Pennsylvania, South Carolina, Georgia, and
Alabama. The variation in index values for this component is very
high, next only to the social component. This implies that a great
deal of improvement in the health and education fields can be made
among the SMSA's so that regional differentials in health and education
quality may be eliminated.
170
-------
The social component received the highest coefficient of variation,
0.71, indicating that a wide range of social factors are found in
varying levels of quality over all medium SMSA's in this country. The
East North;Central Region and the Pacific region had the most "A" and "B"
ranking SMSA's, while those in the four southern states rated markedly
below average.
In comparison, the medium SMSA's jointly display clearer geographic
patterns in terms of quality of life ratings than the large SMSA's.
The variations in the composite indexes are high for the health and
education component and the social component and relatively low for
the other quality of life components in both size groups. However,
the trade-off hypothesis of quality of life components between the
results of industrialization and environmental quality is much more
discernible in the large SMSA's than in the medium SMSA's. The two
methods employed to compute the ratings and rankings also demonstrated
significant consistency between rankings for the medium group SMSA's
as they were for the large SMSA's. The rank-order correlation co-
efficients for the five quality of life components are, respectively,
0.94, 0.96, 0.92, 0,98, and 0.97.
171
-------
CHAPTER VII
QUALITY OF LIFE FINDINGS AND IMPLICATIONS;
SMALL METROPOLITAN AREAS (S)
By definition of the U.S. Department of Commerce, there were 95 SMSA's
in this country with a population smaller than 200,000 in 1970. Most
of these SMSA's are geographically concentrated in the East North
Central and the West South Central regions, especially in the State
of Texas. There are only two SMSA's on the West Coast and seven in the
Mountain area. The remaining are scattered through New England, the
West North Central, and the South. Although the quality of life
factors selected to assess the level of quality inputs in the small
SMSA's are identical to those employed in the large and medium SMSA's,
some factors have been excluded either because of incomplete data or
because data were not available at all. Sometimes estimated data
were used in order to complete the overall evaluation. Those estimated
data are marked with dots as shown in the tables in the Appendix.
The five quality of life components will be presented in this chapter
in a like manner to the preceding two chapters. In passing, it should
be noted again that only the relative ratings for the SMSA, not the
indexes themselves, can be compared with those in the preceding two
chapters, since the factor means used to compute the indexes are
different. Specifically with respect to the index values of SMSA's no
comparison should be made other than with those SMSA's in the same
group.
ECONOMIC COMPONENT
Out of the 95 small SMSA's, 13 outstanding were identified. More
than 30 SMSA's in the group were classified as excellent. In other
words, the economic component composite indexes for the small SMSA's
tend to be more clustered in the "B" category than in any others.
With 21 substandard SMSA's, the number remaining for "adequate" and
"good" is apparently small. What this amounts to is that economically
this group of small SMSA's is either relatively rich, affluent, and
viable for growth or substandard, unhealthy and impeded by obstacles
to industrial development.
172
-------
TABLE 11
INDEX AND RATING OF ECONOMIC COMPONENT (S)
Adjusted Standardized Score!
14».
150.
151,
152.
153.
154.
155.
156.
157.
158.
159.
160.
161.
162.
163.
164.
165.
1«6.
167.
166.
169.
170.
171.
172.
173.
174.
175.
176.
177.
178.
179.
ISO.
181.
182.
183.
184.
185.
186.
187.
188.
189.
190.
191.
192.
193.
194.
195.
196.
197.
198.
SBA
Abilene, Texae
Albany, Ga.
Al toons, It.
Aawrlllo, Texaa
Andereon, Ind.
Aaherllle, N.C.
Atlantic City, N.J.
Bay City, Mich.
Billings, Mont.
Blloxl-Gulfport. Miss.
BlooBlngton-Noraal, 111.
Bola« City, Idaho
Brlatol, Conn.
Brockton, Maaa.
Brownsville-Harlingen-San Benlto,
Texaa
Bryan-College Station, T«xa>
Cedar Raptda. low
Cha^slgn-Urbana. 111.
Coluabla, Ho.
Danbury, Conn.
Decatur, 111.
Dubuqua, Iowa
Durham, N.C.
Fall River, Mi... -R.I.
Fargo-Hoorhead, N. Dak. -Minn.
fltchburg-Leomlnster, Maaa.
Fort Smith, Ark.-Okla.
Gadaden, Alabaaa
Galneavllle, Fla.
Galveaton-Taxaa City, Texaa
Great Falla, Mont.
Green Bay, Via.
Jackaon, Mich.
Kenoaha, Win.
La Croaae, Ui«.
Lafayette, La.
Lafayette-Weat Lafayette, Ind.
Lake Charles, La.
Laredo, Texaa
Lawton, Okla.
Lewlaton-Auburn, Maine
Lexington, Ky.
Liu, Ohio
Lincoln, Nebraska
Lubbock, Texa.
Lynch burg, Va.
Manchester, N.R.
Manaflald, Ohio
McAllen-Pharr-Edlnburg, Texas
Merlden, Conn.
Value
1.9214
0.4643
1.2143
2.7500
2.3429
1.9000
0.7643
2.3071
1.8429
0.5857
1.9000
2.3857
2.2571
1.1786
0.2714
1.6643
2.3214
1.4786
1.5214
2.1429
2.5929
1.9857
1.8786
1.1214
1.7929
1.6929
0.9929
0.8429
0.9214
2.1357
0.8643
2.3429
2.2143
1.9643
2.1000
0.8500
2.1429
1.1500
0.0571
0.6000
0.9571
1.9357
1.7071
2.7571
2.0214
2.0429
2.0571
2.0214
0.5071
1.9429
Rank
45
93
70
3
16
47
86
20
50
91
46
14
24
71
94
63
19
69
68
28
7
38
49
74
52
60
77
85
81
30
83
17
26
40
31
84
29
73
95
90
78
44
57
2
34
33
32
35
92
41
Rating
B
E
0
A
B
C
E
B
C
E
C
B
B
D
I
C
S
D
D
B
A
B
C
D
C
C
E
E
E
B
E
B
B
B
B
E
B
B
E
E
E
B
C
A
B
B
B
B
E
B
Standardized Scores
Value
0.2116
-0.8210
-0.2996
0.6064
0.6297
0.0663
-0.5729
0.3176
0.0776
-0.6833
0.1957
0.3665
0.2040
-0.2854
-1.5980
-0.0463
0.3454
-0.2260
-0.1695
0. 3264
0.4347
0.1982
-0.0056
-0.4919
-0.0604
0.0059
-0.4156
-0.6246
-0.5426
0.3669
-0.4649
0.3922
0.3901
0.2116
0.2496
-0.4808
0.2106
-0.3171
-1.8953
-0.8447
-0.4968
0.1674
0.1152
0.6347
0.1591
0.1097
0.2830
0.1192
-1.5788
0.2211
Rank
33
91
71
8
7
51
85
27
50
89
38
20
36
70
94
62
22
69
68
26
14
37
58
80
63
56
75
86
82
19
77
17
18
34
31
79
35
72
95
92
81
40
46
6
41
47
28
45
93
32
Ratlin.
B
E
D
A
A
C
E
B
C
E
t
B
B
D
E
C
B
D
D
B
B
B
C
D
C
C
D
E
E
B
D
B
B
B
B
D
B
D
E
E
D
B
C
A
B
C
B
C
E
B
173
-------
TABLE 11 (Concluded)
INDEX AND RATING OF ECONOMIC COMPONENT (S)
Ad \u*ted StMndtrdiced Seoret
199.
200.
201.
202.
203.
204.
205.
206.
207.
208.
209.
210.
211.
212.
213.
214.
215.
216.
217.
2J8.
219.
220.
221.
222.
223.
224.
225.
226.
227.
228.
229.
230.
231.
232.
233.
234.
235
236.
237.
238
239.
240.
241.
242.
243.
A -
B -
SMSA
Midland, Texas
Modeato, Calif.
Monroe, la.
Muncle, Ind.
Muskegon-Muskegon Heights, Mich.
Nashua, N.H.
New Bedford, Mass.
New Britain, Conn,
Norwalk, conn.
Odessa, Texas
Ogdcn, Utah
Cvensboro, Ky.
Petersburg-Colonial Heights, Va.
Pine Bluff, Ark.
Plttsfield, Mass.
Portland, Maine
Provo-Orem, Utah
Pueblo, Colo.
Racine, Via.
Reno, Nev.
Roanoke, Va.
Rochester, Minn.
St. Joseph, Mo.
Salem, Oreg.
San Angelo, Texas
Savannah, Ga.
Shernutn-DeniBon, Texas
Sioux City, Iowa-Nebraska
Sioux Falls, S. Dale.
Springfield, 111.
Springfield, Mo.
Springfield, Ohio
Steubenville-Weirton, Ohio-W. Va
Tallahassee, Fla.
Terre Haute, Ind.
Texarkana, Texas- Ark.
Topeka, Kans.
Tuscaloosa, Alabama
Tyler, Texas
Vineland-Millvllle-Bridgeton, N.
Waco, Texas
Waterloo, Iowa
Wheeling. W. Va.-Ohlo
Wichita Falls, Texas
Wilmington, N.C.
Outstanding (s x + s)
Excellent (R + .28s < B < X + s)
Value Rank
2.7143
1.7929
1.1571
2.3286
1.7857
1.6857
1.0500
1.7786
2.6214
2.3714
1.6143
1.7000
1.0571
0.6929
1.8429
1.7786
0.7071
1.6429
2.4214
2.5071
2.5143
1.5571
2.2500
2.2786
2.4214
0.9214
2.2714
1.7000
1.8857
2.4643
2.4857
2.0143
2.0143
1.5286
2.2000
1.9429
2.6857
0.7286
2.7643
J. 0.8929
1.9786
1.9357
1.6786
2.3071
0.9571
Mean (x)
Rat lot
4
53
72
18
54
61
76
55
6
15
65
58
75
89
51
56
88
64
13
9
8
66
25
22
12
80
23
59
48
11
10
36
37
67
27
42
5
87
1
82
39
43
62
21
79
• 1.7372
A
C
D
g
C
C
E
C
A
B
C
C
E
E
C
C
E
C
A
A
A
C
C
C
A
A
B
B
D
B
B
A
E
A
E
B
B
C
B
E
Standard Deviation (s) - 0.643
Stanrfjrrrflced Score*
V..LU.
1.1825
-0.1692
-0.4373
0.4815
0.0835
-0.0286
-0.5561
-0.0056
0.9004
0.5761
-0.0938
-0.0384
-0.4115
-0.6492
0.1332
0.0433
-0.6624
-0.0445
0.4329
1.0243
0.4695
0.0074
0.3381
0.2546
0.5206
-0.4688
0.3437
0.1259
0.0585
0.4075
0.4866
0.0820
0.3663
-0.1376
0.2619
0.0399
0.8234
-0.7510
0.7159
-0.5485
0.1698
0.1302
-0.0709
0.3322
-0.4136
Rank
1
67
76
12
48
59
84
57
3
9
65
60
73
87
42
53
88
61
15
2
13
55
24
30
10
78
23
44
52
16
11
49
21
66
29
54
4
90
5
83
39
43
64
25
74
Biting
A
D
D
B
C
C
E
C
A
A
C
C
D
E
C
C
E
C
B
A
B
C
B
B
A
D
B
C
C
B
8
C
B
C
B
C
A
E
A
E
,
C
C
B
D
Mean <£) » 0.0000
Standard Deviation (s) • 0.5202
C - Good (5 - ,28s < C < x + .28s)
D - Adequate (x-s
-------
Among the 13 outstanding SMSA's four are in Texas; with an index of
2.76, or about 1.57 standard deviations above the mean of 1.74, Tyler
is one of three which scored the highest. The other three in the state
are Amarillo, Midland, and San Angelo; they ranked, respectively,
third, fourth, and 12th. These four SMSA's are characterized by high
ratings of the individual economic well-being index in terms of average
income and wealth, and low ratings in the degree of economic concentra-
tion and unequal income distribution. Therefore, the economic structure
in the SMSA's is concentrated; however, the relatively unequal dis-
tribution of income and wealth among residents in the SMSA's does have
important political implication and is worth noting. For instance,
despite the fact that Midland had the highest income per capita
adjusted for living cost among the 95 SMSA's in 1970, it still had a
very high percentage of families with income below the poverty level--
one of every 10 families had income below the poverty level. The
corresponding figures were 12.9 percent, 9.1 percent, and 14.6 percent
respectively in Tyler, Amarillo, and San Angelo.
The remaining outstanding SMSA's are Lincoln (Nebraska), Topeka (Kansas),
Norwalk (Connecticut), Decatur (Illinois), Roanoke (Virginia),
Reno (Nevada), Springfield (Missouri), Springfield (Illinois), and
Racine (Wisconsin). For these SMSA's, the impact of their state
governments and the governments' employment on the regional economy
would seem to be significant.
Three SMSA's in southern Texas along with those SMSA's in the southern
states are rated substandard economically. In vivid contrast to the
SMSA's in the northern part of the State of Texas, Laredo and
Erownsville/Harlingen/San Benito ranked at the bottom of the list.
McAllen/Pharr/Edinburg, with an index slightly higher than that for
Albany (Georgia), came up as the fourth-lowest rated SMSA in the group.
The index for Laredo is 0.06 or 2.6 standard deviations below the group
mean. For McAllen/Pharr/Edinburg, it is 0.51 or 1.9 standard devia-
tions lower than the mean. Apparently the extremely low personal in-
come per capita and the weak economy in these SMSA's are generally
expected. As shown in Table C-l in the Appendix, the average personal
income per capita in 1970 was $1,573, $1,580, and $1,523, respectively,
for Laredo, Brownsville/Harlingen/San Benito and McAllen/Pharr/Edinburg;
this was just about 50 percent of the average personal income in the
United States in 1970. The high unemployment rates, low labor produc-
tivity, and housing values, etc., worsen the quality of economic life
in these SMSA's. The dichotomized economic situation unveiled in the
State of Texas was also observed for the entire eastern half of the
United States. As shown in Figure 11, there are no excellent or out-
standing SMSA's found in the southern states east of the Mississippi
River, and almost all of the SMSA's in the Great Lakes area are rated
better than "good." While industrialization achieved the high economic
175
-------
176
-------
CHART 11
REGIONAL VARIATIONS IN INDEXES:
ECONOMIC COMPONENT (S)
ADJUSTED STANDARDIZED SCORE
I Tyler. Texas
2 Lincoln, Nebr.
3 Amorillo, Texos
4 Midlond, Texas
5 Topeko, Kons
6 Norwolk. Conn
7 Decotur. III.
8 Roanoke, Va.
9 Reno. Nev
10 Springfield, Mo.
I! Springfield. III.
12 San Angela, Texas
13 Racine. Wi»
f 14 Boise City. Idaho
15 Odessa, Texas
16 Anderson. Ind
17 Green Boy. Wis
18 Muncie. Ind.
19 Cedar Rapids. Iowa
20 Boy City, Mich
21 Wichita Falls. Texas
22 Salem, Oreg
23 Sherman - Denison, Texas
24 Bristol, Conn
25 St.Joseph. Mo.
26 Jockson. Mich.
27 Terre Haute, Ind
28 Donbury, Conn
29 Lafayette - West Lafayette, Ind.
30 Golveston - Texas City, Texas
31 La Crosse. Wis
32 Manchester, N.H.
33 Lynchburg, Vo
34 Lubbock, Texas
35 Monsfield, Ohio
36 Springfield, Ohio
37 Steubenville - Weirton, Ohio - VV.Vo.
38 Dubuque, Iowa
39 Waco. Texas
40 Kenosha, Wis
41 Meriden, Conn.
42 Texarkona, Texas - Ark.
43 Waterloo, Iowa
44 Lexington, Ky.
45 Abilene. Texas
' 46 Bloomington - Normal. Ill
47 Ashew/le. N C
48 Sioux Falls. S.Dak
49 Durham, N.C.
50 Billings, Mont
51 Pittsfield, Mass.
52 Fargo - Moorheod, N.Dok. - Minn.
53 Modesto, Calif.
54 Muskegon - Muskegon Heights, Mich.
55 New Britain. Conn.
56 Portland, Maine
57 Lima, Ohio
58 Owensboro, Ky
59 Sioux City, Iowa - Nebr.
60 Fitchburg - Leominster, Moss
61 Nashua, N H.
62 Wheeling, W Vo - Ohio
63 Bryan - College Station, Texas
64 Pueblo. Cola
65 Ogden, Utah
k 66 Rochester, Minn
f 67 Tallahassee, Flo.
68 Columbia, Mo
69 Champaign - Urbano, III.
70 Altoono. Po.
71 Brockton. Moss.
72 Monroe, La
73 Lake Charles, La
. 74 Fall River. Moss. - R I
f 75 Petersburg - Colonial Heights. Vo.
76 New Bedford. Mass
77 Fort Smith, Ark. - Okla.
78 Lewisfon - Auburn, fVaine
79 Wilmington, N.C.
80 Savannah , Ga.
81 Gainesville. Flo.
82 Vinelond - M.llville - Bndgeton, N.J.
83 Great Falls, Mont
84 Lafayette, La.
85 Godsden, Ala
86 Atlantic City. N J
87 Tuscoloosa. Ala.
88 Prove - Orem. Utah
89 Pine Bluff. Ark
90 Lowton. Ok la.
91 Biloxi - Gullporl, Miss
92 McAllen - Phorr - Edinburs. T.xos
93 Albony, Go.
94 Brownsville - Harhngen - Son B«nito, Texas
95 Laredo, Texas
X - S
177
X- S
X -M.OO •= 1.7372
S = Standard Deviation
X +S
= 0 6491
-------
status in the latter area, the weak economic structure, low labor
productivity, and scarcity of investments are common causes of
poverty in the former region. This striking difference between re-
gional economic strengths in the U.S. is more distinguished for small
SMSA's than for medium and large SMSA's, when they are compared on a
relative basis. The remaining "E" rated SMSA's are Biloxi/Gulfport
(Mississippi), Lawton (Oklahoma), Pine Bluff (Arkansas), Provo/Orem
(Utah), Tuscaloosa (Alabama), Atlantic City (New Jersey), Gadsden
(Alabama), Lafayette (Louisiana), Great Falls (Montana), Vineland/
Millville/Bridgeton (New Jersey), Gainesville (Florida), Savannah
(Georgia), Wilmington (North Carolina), Lewiston/Auburn (Maine),
Fort Smith (Arkansas/Oklahoma), New Bedford (Massachusetts), and
Petersburg/Colonial Heights (Virginia).
The long bars centering on both ends of the bar chart as illustrated
in Chart 11 clearly indicate the strong, healthy positions of the
SMSA's in the upper portion and the much more desparate conditions
of the SMSA's at the lower part. Not only is the standard deviation
of the index values high, but also the coefficient of the variation
of indexes is large, i.e., 37.4 percent which is much larger than the
coefficients computed for the economic component for the medium and
large size SMSA's. The implication of this is that the economic
quality of life experienced by the people in the small SMSA's is
relatively more unequal than that by the people in the larger SMSA's.
POLITICAL COMPONENT
Regional variations in political quality of life in the small SMSA's
is even more striking than in the economic quality of Life comparison.
A dividing line can be drawn from Modesto (California) through
Pueblo (Colorado), Springfield (Missouri), Terre Haute (Indiana),
Wheeling (West Virginia/Ohio) to Atlantic City (New Jersey). There
is not a single "E" rated SMSA north of the line, but south of the
line, no SMSA has been classified as either "excellent" or "out-
standing," except Midland (Texas). In the preceding discussion on
economic well-being, one notes that there are more "E" than "A"
rated SMSA's. In this political section, "A" rated SMSA's account
for more than one-fifth of the total and outnumber the "E" rated.
As shown in Table 12, the indexes for the SMSA's are such that 43
SMSA's, or 46.2 percent of the total, have index figures exceeding the
mean plus 0.28 standard deviation, and hence, are rated either excellent
or outstanding. This implies that, based on the political considera-
tions, many more small SMSA's are relatively better off than they were
when judged from the economic standpoint.
178
-------
TABLE 12
INDEX AND RATING OF POLITICAL COMPONENT (S)
149.
150.
151.
152.
153.
154.
155.
156.
157.
158.
159.
1(0.
161.
162.
163.-
164.
165.
1(6.
167.
168.
169.
170.
171.
172.
173.
174.
175.
176.
177.
178.
179.
180.
181.
1S2.
183.
184.
183.
186.
187.
188.
189.
190.
191.
192.
193.
194.
195.
196.
197.
198.
SNSA
Abilene, Texas
Albany, Ga.
Altoona, Pa.
Aaiarillo, Texas
Anderson, Ind.
Ashevllle, N.C.
Atlantic City, N.J.
lay City, Mich.
Billings. Mont.
Blloxl-Gulfport, Hiss.
Blooming ton-Noroal , 111.
Boise City, Idaho
Bristol, Conn.
Brockton, Mass.
Brownavllle-Harllngen-San Benlto,
Texas
Bryan-College Station, Texas
Cedar Rapids, Iowa
dueipalgn-Urbana. Ill'
Columbia, Ho.
Danbury, Conn.
Decatur, 111.
Dubuque , Iowa
Durban, N.C.
Fall liver, Haas. -R.I.
Fargo-Moorheed, N. Dak.-Hlnn.
Fltchburg-Leoalnster, Haas.
Fort Smith, Ark.-Okls.
Gadaden, Alabama
Gainesville, Fla.
Galveaton-Texas City, Texas
Great Falla, Mont.
Green Bay, Via.
Jackaon, Hlch.
Kenoaha, Via.
La Croase, His.
Lafayette, La.
Lafayette-West Lafayette, Ind.
Lake Charles, La.
Laredo, Texas
Lawton, Okla.
Lewiaton-Aubura, Maine
Lexington, Ky.
Lima. Ohio
Lincoln, Nebraska
Lubbock, Texaa
Lynchburg, Va.
Manchester, H.H.
Mansfield, Ohio
HcAllen-Eharr-Edlnburg, Texas
Merlden, Conn.
Adjusted
Value
1.8929
1.4008
2.5476
2.2857
3.1905
2.4683
3.3214
3.6151
3.3095
1.9087
2.9246
3.2817
3.1349
2.8333
1.2222
2.0714
3.1508
2.0873
2.5873
3.6190
2.6151
3.3651
2.0317
2.8016
3.3651
3.3333
1.5159
2.0873
1.7619
2.1706
2.4643
3.3849
2.8373
2.9643
3.8016
1.6190
3.0675
1.7976
1.3690
1.3730
2.8810
2.0516
2.7579
2.8016
2.2857
2.1548
3.3532
2.6071
1.3413
3.3532
Standardised Scores
Rank
82
89
56
64
21
58
17
4
18
81
39
19
24
43
95
77
23
75
55
3
51
11
80
44
12
16
88
76
85
72
59
9
42
35
1
87
28
84
91
90
40
78
46
45
65
74
14
53
92
15
Eating
E
E
C
D
B
C
A
A
A
E
,
A
B
B
I
D
B
D
C
A
C
A
D
C
A
A
1
D
E
D
C
E
E
B
D
C
C
D
D
A
C
E
A
Standardized Scores
Value
-0.5093
-1.0381
-0.0699
-0.2076
0.3449
-0.0437
0.6483
0.5908
0.3823
-0.6434
0.0928
0.4193
0.3947
0.1661
-1.1802
-0.2116
0.2951
-0.1567
0,0655
0.5003
0.0433
0.4273
-0.3463
0.2755
0.4166
0.3091
-0.6980
-0.4026
-0.6729
-0.3022
-0.0445
0.5004
0.2792
0.3429
0.7718
-0.5899
0.2700
-0.5020
-1.2235
-0.9506
0.0562
-0.4619
0.0887
0.0600
-0.2250
-0.2042
0.3248
-0.1137
-1.0807
0.2970
Rank
83
92
60
67
22
55
4
6
20
85
44
16
18
42
94
68
30
64
47
11
52
15
76
34
17
28
87
79
86
75
56
10
33
23
2
84
35
82
95
90
50
81
46
48
71
66
25
63
93
29
Rating
E
E
C
D
B
C
A
A
B
E
C
B
B
B
E
D
B
D
C
A
C
B
D
B
B
B
E
D
E
D
C
A
«
B
A
E
B
E
E
I
C
E
C
C
D
D
B
C
E
B
179
-------
TABLE 12 (Concluded)
INDEX AND RATING OF POLITICAL COMPONENT (S)
Ad lusted Standardized Scorea
SMS_A Value J
199.
200.
201.
202.
203.
204.
205.
206.
207.
208.
209.
210.
211.
212.
213.
214.
215.
216.
217.
218.
219.
220.
221.
222.
223.
224.
225.
226.
227.
228.
229.
230.
231.
232.
233.
234.
235.
236.
237.
238.
239.
240.
241.
242.
243.
A -
Midland, Texas
Modesto, Calif.
Monroe. La.
Muncle, Ind.
Muskegon-Muskegon Heights, Mich.
Nashua, N.H.
New Bedford, Mass.
New Britain, Conn.
Norwalk, Conn.
Odessa, Texas
Ogden, Utah
Owensboro, Ky.
Petersburg-Colonial Heights, Va.
Pine Bluff, Ark.
Plttsfield, Mass.
Portland, Maine
Provo-Orem, Utah
Pueblo, Colo.
Racine, Wla.
Reno, Nev.
Roanoke, Va.
Rochester, Minn.
St. Joseph, Mo.
Salem, Oreg.
San Angelo, Texas
Savannah, Ga.
Sherman-Denison, Texas
Sioux City, Iowa-Nebraska
Sioux Falls, S. Dak.
Springfield, 111.
Springfield, Mo.
Springfield, Ohio
Steubenvllle-Welrton, Ohlo-W. Va.
Tallahassee, Fla.
Terre Haute, Ind.
Texarkana, Texas-Ark.
Topeka, Kans.
Tuscaloosa, Alabama
Tyler, Texas
Vineland-Mll Ivl lie -Bridgeton , N.J .
Waco, Texas
Waterloo, Iowa
Wheeling, W. Va.-Ohio
Wichita Falls, Texas
Wilmington, N.C.
Outstanding (a x + a)
2.
2.
I.
3.
3.
3.
2.
2.
3.
2.
3.
2.
2.
1.
3.
3.
2.
3.
3.
2.
2.
3.
2.
2.
2.
1.
2.
3.
3.
3.
2.
2.
3.
2.
3.
2.
9484
8690
8333
1706
4127
0833
9563
6190
0476
2143
4960
2302
3333
3214
6627
0079
5913
3770
0278
6111
4365
0675
6865
6905
1865
6429
,4643
0913
3889
.0040
.9444
,4643
,0873
.7302
.6111
.1825
3.2579
1.
2.
2 (
2.
3.
3.
2.
.3214
.2540
,4881
,1627
,0000
,3571
.0357
2.2500
Mean
Standard
lank Rating
37
41
83
22
7
27
36
50
30
69
6
68
63
93
2
32
54
10
31
52
62
29
49
48
70
86
60
25
8
33
38
61
26
47
5
71
20
94
66
57
73
34
13
79
67
(x) - 2.6293
Deviation (s)
B
I
E
B
A
B
B
C
B
D
A
D
D
E
A
B
C
A
B
C
D
B
C
C
D
E
C
B
A
B
B
C
B
C
A
D
B
E
D
C
u
8
A
D
D
- 0.6464
Standardized Scorea
V«lu.
0.1586
0.5974
-0.3931
0.2882
0.6626
0.3191
0.3600
0.0448
0.2069
-0.2540
0.4369
-0.0002
-0.2172
-0,7065
0.8415
0.4945
-0.0597
0.4822
0.3109
-0.0561
-0.0675
0. 3872
-0.0273
0.0897
-0.2198
-0.7386
-0.1901
0.2409
0.5857
0.2521
0.0569
-0.0716
0.2216
0.1721
0.5044
0.2020
0.3273
-1.0331
-0.2920
-0.1091
-0.3866
0.2819
0.5H6
-0.4149
-0.2842
Mean
Standard
Rank
43
5
78
31
3
26
21
51
39
72
14
53
69
88
1
12
58
13
27
57
59
19
54
45
70
89
65
37
7
36
49
61
38
41
9
40
24
91
74
62
77
32
8
80
73
(x) - 0.0000
Deviation (s)
Rating
,
A
D
B
A
B
B
C
B
D
B
C
D
E
A
A
C
A
B
C
C
8
C
C
D
E
D
B
A
B
C
C
B
B
A
B
B
E
D
C
D
B
A
D
D
- 0.4583
B - Excellent (5 + .28s < B < x + s)
C " Good ( S - .28s < C < x + .28s)
D " Adequate (5 - s < t> < x - .28s)
E * Substandard (£ x - s)
180
-------
La Crosse (Wisconsin) received the highest political component index
of 3.80, or about 1.8 standard deviations above the mean. Next are
Pittsfield (Massachusetts), Danbury (Connecticut), Bay City (Michigan),
Terre Haute (Indiana), Ogden (Utah), Muskegon/Muskegon Heights
(Michigan), Sioux Falls (South Dakota), Green Bay (Wisconsin), and
Pueblo (Colorado), which make up the top 10 SMSA's. It is very
surprising to note that none of these 10 SMSA's was mentioned as out-
standing in the economic component, though some were rated as excellent.
In fact, Ogden, Sioux Falls, and Pueblo were shown to be only adequate
or "good" economically. The per capita income in La Crosse in 1970
was 47th when compared to others. In this case, the usual assertion
that the quality of political life must be tied to the strength of
economic achievements seems to lose ground.
It is aptly evident from the earlier discussions that there exist
various problems, even in the outstanding SMSA's, although they are
not as serious as those found in the lower rated SMSA's. In other
words, even in the outstanding or excellent SMSA's, courses of action
can be taken to improve the quality of life or to reduce the relatively
less desirable conditions influencing quality of life. For instance,
people in La Crosse could be better informed through public and private
information channels and be more active in participating in political
activities, etc.; residents in Pittsfield would enjoy even better
political quality of life if the professionalism and performance of
the local governments can be enhanced; Danbury would score much higher
if its ranking in local government professionalism were higher than
64th, e.g., the property crime rate in the SMSA might be lowered from
2,762.8 per 100,000 population in 1970 (the corresponding rate in the
U.S. was 2,431.8) if it had better quality or better paid patrolmen.
(The entrance salary of patrolmen in this SMSA was about $300 below
the U.S. average.)
Brownsville/Harlingen/San Benito, McAllen/Pharr/Edinburg, and Laredo
in Texas, showing the least favorable indexes in economic well-being
in the last section, were no exception in the political component
evaluation. In addition to these three SMSA's, many with substandard
economic ratings are shown in Table 12 as substandard, such as
Pine Bluff, Lawton, Fort Smith, Lafayette, Savannah, Gainesville, and
Biloxi/Gulfport. Nevertheless, the number of SMSA's in this group is
smaller than that in the economic component. The lowest 10 indexes in
this component do not differ significantly from each other, meaning
that the composite evaluation on political backwardness for the 10
SMSA's is about equal. However, their individual weaknesses, to a cer-
tain degree, are still varying among the SMSA's.
181
-------
While people in Lawton tended to be less active in political activities
than those in Pine Bluff, the local governments in the former SMSA
compared even less effectively than in the latter SMSA. as far as the
performance of the governments is concerned. The lower crime rates
and more efficient fire protection services in Pine Bluff would
probably be ascribed to the relatively high paying jobs of patrolmen
and firemen. In terms of the welfare system and the associated pay-
ments, welfare recipients in Lawton were treated relatively better
than recipients in Pine Bluff.
Although Biloxi/Gulfport (Mississippi) had low rankings in almost all
political considerations, it ranked incredibly high among the 95
SMSA's from the viewpoint of local government performance. Its very
low income rates and the very low entrance salary of patrolmen suggest
that crime rates are not necessarily related to the high salaries of
policemen. It is one of the SMSA's which received from the Federal
Government the highest percentage of revenues, i.e., more than one-
fifth of its total local revenues in 1970 were federal funds. Despite
the low salaries for teachers (the average monthly earnings of
teachers in the area was $442, only 64.9 percent of the U.S. average),
its percentage of persons 25 years old and over who have completed
4 years high school education or more was higher than the U.S. average,
54.7 percent versus 52.3 percent. There were fewer males ages 16 to
21 who were not high school graduates. Economically this area was
not wealthy, but its unadjusted expenditures on health amounted to
$3.88 per capita, or about one-third above the national level. As a
result of these factors, the local government performance in Biloxi/
Gulfport was rated outstanding when compared to the other 94 small SMSA's,
The variation in index values, smaller in this component than in the
economic component, is clearly illustrated in Chart 12. The standard
deviation for the component was computed at 0.65, just about the size
for the economic component, but the coefficient of variation was
24.7 percent, almost 13 percentage points below that for the economic
component since the mean value for this component was more than 51-1
percent higher than that for the economic component. This implies a
smaller variation within the SMSA's when political factors are com-
pared than when economic factors are compared.
When intergroup comparison between the large, medium, and small groups
of SMSA's are made on a geographic basis, Figures 2, 7, and 12 show
that the political component is the only quality of life component
which does not have a higher than "excellent" rating for any SMSA in
the southern states.
182
-------
SMSA
CHART 12
REGIONAL VARIATIONS IN INDEXES:
POLITICAL COMPONENT (Sj
ADJUSTED STANDARDIZED SCORE
X-S
1
2
3
4
5
6
7
8
9
10
II
12
13
14
15
16
17
18
1'
f 20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
lo Go»«. Wi$.
Pitttfield. Mots.
Donbury, Conn
Boy City. Mich
Terre Haute. Ind
Ogden, Utoh
Muskegon - Muskegon Heights, Mich.
Sioux Falls, 5 Dak.
Green Bay. Wit.
Pueblo, Colo.
Dubuque. Iowa
Fargo - Moorheod, N.Dok. - Minn.
Wheeling, W.Va. - Ohio
Manchester. N.H.
Meriden. Conn.
Fitchburg - Leominster, Mast,
Atlantic City. N.J.
Billings, Mont.
Boise City, Idaho
Topeka. Kans.
Anderson, |nd.
Muncie, Ind.
Cedar Rapidj, Iowa
Bristol, Conn
Sioux City, Iowa - Nebr.
Sfeubenville - Weirton. Ohio - W.Va,
Nashua. N H
Lafayette - West Lafayette, Ind.
Rochester, Minn.
Norwolk, Conn
Racine. Wis.
Portland. Maine
Springfield, III
Waterloo, Iowa
Kenoiha. Wis
New Bedford, Moss.
Midland, Texas
Springfield, Mo.
Bloomington - Normal, II!
Lewiston - Auburn, Maine
Modesto, Calif
Jackson, Mich.
Brockton, Mass
Fall River, Mass - R I
Lincoln, Nebr.
Lima. Ohio
Tallahassee, F/o.
Salem, Oreg
St. Joseph. Mo.
New Britain, Conn
Decatur, III.
Reno, Nev.
Mansfield, Ohio
Provo - Orem, Utah
Columbia, Mo
Altoono, Pa.
V.neJond - Mt'MJJe -Sridgetan, N.J.
Asheville, N C
Great Falls, Mont
Sherman - Denison. Texas
Springfield, Ohio
Roanoke, Va.
Petersburg - Colonial Heights, Va.
Amorillo, Texas
Lubbock, Texas
Tyler, Texas
Wilmington. N C
Owensboro, Ky.
Odessa, Texas
Son Angela, Texas
Texarkana, Texas - Ark
Golveston - Texas City, Texas
Waco, Texas
Lynchburg. Vo.
Champaign- Urbana, III
Gadsden, Ala
Bryan - College Station, Texas
Lexington, Ky
Wichita Falls, Texas
Durham, N C
B.loxi - Gulfport, Miss.
Abilene, Texas
Monroe, Lo
Lake Charles, La
Gainesville, Flo
Savannah, Go
Lafayette. Lo
fort Smith, Ark. - Olc/a.
Albany, Go
Lowton. Okla
Laredo, Texas
McAMen - Phofr - Edinburg, Texot
Pine Bluff, Ark
Tuscaloosa. Ala
Brownsville - Harhngen - San Benito, Texas
x+s
183
K = Mean - 2.6293
S ~ Standard Deviatio
-------
03
D
cr
0)
•n
184
-------
ENVIRONMENTAL COMPONENT
Environmental quality evaluation for the large and medium groups of
SMSA's was shown to be favorable to the Pacific region. The trade-
off between industrialization and environmental deterioration was
described to be very obvious among the large SMSA's in the Great Lakes
area, and this relationship was also evident from the medium sized
SMSA's, though to a lesser degree. When the small SMSA's are compared,
the trade-off pattern, if it exists at all, does not seem to be very
significant. This is due mainly to either of the following two
reasons. First, this finding may be in fact true, i.e., there is
little trade-off associated between growth and ecology in the small
SMSA's. Second, the finding may be misleading because many environ-
mental factors employed in the preceding evaluations are not included
in this chapter, due to the nonavailability of data. For example,
there is no readily available information on air pollution and climate
for many small sized SMSA's. Consequently these factors are not
shown in the concerns with individual and institutional environment
and natural environment.
Based on available information on various levels of pollution other
than air, and the recreational areas and facilities, the 95 small
SMSA's were evaluated according to the original formula in which
natural environment was weighted equally with the individual and
institutional environment. As a result, the evaluation was in favor
of SMSA's with greater areas and facilities for recreation, and less
emphasis was placed on each type of pollution. Bearing in mind these
precautions about limited information, Table 13 represents the over-
all evaluation of environmental quality among the small SMSA's.
Jackson in Michigan and San Angelo in Texas, ranked at the top of
the outstanding group, followed by four SMSA's in the New England
region--Fitchburg/Leominster and Pittsfield in Massachusetts, and
Meriden and Bristol in Connecticut. Jackson, San Angelo, Fitchburg/
Leominster, and Meriden each had an index greater than the group
mean plus 2.0 standard deviations. While Jackson had very low
visual pollution and very high recreational areas and large facilities,
San Angelo had even better ratings in those categories. However, the
latter had the worst water pollution problem. Although noise pollu-
tion was probably not in existence at all in Fitchburg/Leominster, the
SMSA had above average problems in visual pollution and solid waste
generation. Pittsfield SMSA also suffered from greater than average
problems of visual and water pollution. While people in Meriden and
185
-------
TABLE 13
INDEX AND RATING OF ENVIRONMENTAL COMPONENT (S)
Adjusted Standardised Scores
Standardized Scorei
149.
150.
131.
152.
15J.
134.
155.
156.
137.
1)8.
159.
160.
161.
162.
163.
164.
165.
166.
167.
166.
169.
170.
171.
172.
173.
174.
175.
176.
177.
178.
179.
I8
-------
TABLE 13 (Concluded)
INDEX AND RATING OF ENVIRONMENTAL COMPONENT (S)
Adjuitid St«ndirdlt«d Scora*
Sttn
-------
Bristol benefited from larger recreational areas and facilities per
capita, the solid wastes generated in these two SMSA's for every $1
million of value added was substantially higher than the rest of the
SMSA's. As contained in Table C-3 in the Appendix, the solid waste
generated in these two areas for every $8 million of value added
totaled 710.5 and 868.1 tons, respectively.
Similarly, the remaining 12 outstanding ranked SMSA's are geographically
scattered among the lower ranking SMSA's, and each of them has its own
outstanding quality factors as well as less desirable environmental
problems. Salem in Oregon, for example, the only western outstanding
SMSA in the environmental component--largely because of its recreational
facilities--suffered from above average problems of noise pollution,
with very high motor vehicle and motorcycle registrations per 1,000
population. Manchester in New Hampshire, as another example, had no
problem at all with noise pollution but in visual pollution, the area
ranked 82nd in the list, 40 percent of its housing units in the central
city being dilapidated, and for every 1,000 people there were only
5.9 acres of parks and recreational areas.
The substandard SMSA's in this component, though equal to the outstanding
group in number--16, are even more scattered throughout the U.S. The
State of Texas had one-quarter of the 16 substandard SMSA's. Together
with Lawton (Oklahoma), Lake Charles (Louisiana), Lafayette (Louisiana),
and Biloxi/Gulfport (Mississippi), they made up one-half of the total
in the South.
Lawton was found economically to be the most backward SMSA in the
group and again appears to be the one with the lowest environmental
quality. Its index of -0.67 is about 2.1 standard deviations below
the mean and is significantly lower than Midland, Texarkana, Lima (Ohio),
and Lafayette (Louisiana)--the five lowest ranking SMSA.'s. Because
of less vehicle and motorcycle registration per 1,000 population,
which is probably due to the area's poverty status, noise pollution
was rated better than average in Lawton. The water pollution index
for the area was 5.13 times as high as the U.S. average, one of the
worst SMSA's in the list.
Midland was the second worst SMSA in the environmental component. It
is located close to the outstanding SMSA San Angelo in Texas. Midland
generated the most solid waste tonnages per million dollars of value
added and had very few parks and recreational areas. In 1970, the
area generated 1,648.6 tons of solid waste per million dollars worth
of value added by manufacturing industries, and each 1.,000 residents
in the region collectively had only 1.5 acres of green areas for
188
-------
D
CHART 13
REGIONAL VARIATIONS IN INDEXES:
ENVIRONMENTAL COMPONENT (S)
SMSA
ADJUSTED STANDARDIZED SCORE
1 Jackson, Mich.
2 San Angela, Texal
3 Fitchburg - Leominster, Mall.
4 Mcriden, Conn.
5 Briltol, Conn.
6 Pittifield. Mail.
7 Salem, Oreg.
8 Tyler, Texas
9 Rochester. Minn.
10 Manchester. N H.
11 St.Joseph. Mo
12 Fort Smith, Ark. - Okla
13 Bloomington- Normal, III.
14 Decatur, III.
15 Gadsden, Ala
16 Waterloo, lowo
17 Provo- Orem, Utah
18 Muskegon - Muskegon Heights, Mich.
19 Sioux City, lowo - Nebr.
20 Asheville, N.C
21 Brownsville - Horlingen - Son Benito, Texas
22 Great Falls. Mont.
23 Green Boy. Wis.
24 Tollohossee, Flo
25 Muncie, Ind.
26 Ogden. Utah
27 Oanbury. Conn.
28 Lincoln, Nebr.
29 Dubuque, Iowa
30 Waco, Texas
31 Modesto, Calif.
.32 Pine Bluff, Ark.
"33 Reno, Nev.
34 Savannah. Go
35 Springfield. Ill
36 Wilmington, N.C.
37 Lynchburg, Vo.
38 Tuscaloosa, Ala.
39 Sherman - Denison, Texas
40 Springfield, Mo
41 Albany, Go.
42 Galvelton - Texas City, Texas
43 Roonoke. Va.
44 Steubenville - Weirton. Ohio - W.Va.
45 Amarillo, Texas
46 Durham, N.C.
47 Lexington, Ky.
48 Petersburg - Colonial Heights, Va.
49 Racine, Wis.
^ 50 Vineland - Millvllle - Bridgeton, NJ.
f 51 McAllen - Pharr - Edinburg. Texas
52 Pueblo. Colo.
53 Terre Haute, Ind
54 Fargo - Moorhead. N.Dok. - Minn.
55 Brockton, Man.
56 Cedar Rapids. Iowa
57 LoCrosse, Wis.
58 Lafayette - West Lafayette, Ind.
59 Norwolk, Conn.
60 Abilene, Texas
61 Anderson, (nd
62 Atlantic City. N.J
63 Kenosha. Wis.
64 Mansfield. Ohio
65 Portland, Maine
66 Topeko, Kans.
67 Wichita Falls. Texas
68 Alloona. Pa.
69 Fall River, Mass. - R.I.
70 New Bedford, Mass.
71 New Britoin, Conn.
72 Odessa, Texas
73 Owensboro. Ky.
74 Sioux Falls, S.Dok.
75 Monroe. Lo.
76 Bryan - College Station, Texas
77 Gomeiville, Flo.
78 Nashua, N H.
79 Springfield, Ohio
f 80 Columbia, Mo
81 Champaign - Urbono, III
82 Billings, Mont.
83 Biloxi - Gulfoort, Miss
84 Boise City. Idaho
85 Lake Charles, La.
86 Wheeling. W Vo. - Ohio
87 Boy City. Mich.
88 Laredo. Texas
89 Lewiston - Auburn, Maine
90 Lubbock. Texas
91 Lafayette, Lo.
92 Lima, Ohio
93 Texarkana. Texas - Ark
94 Midland. Texas
95 Lowton, Okla
X-S
x-s
X + S
189
X-Mean = 0.1592
S - Standard Deviation * 0.4024
-------
190
-------
recreational activities. Texarkana, the SMSA consisting of counties in
both Texas and Arkansas, had the same kind of problem as did Midland
but ranked much better in noise pollution.
Boise City (Idaho) and Billings (Montana) are two "E" rated SMSA's in
the Mountain Region. Boise City ranked eighth in water quality and
Billings third in least solid waste generated per million dollars worth
of manufacturing value added. Their low rankings are thus attributed
to environmental criteria other than water and solid waste pollution.
Lewiston/Auburn is the only substandard area in the entire New England
region which has five outstanding SMSA's. This SMSA had the least
noise pollution as measured by population density in the central city
and the volume of vehicle and motorcycle registration. Like Boise
City and Billings, the component rating of Lewiston/Auburn was
significantly degraded by other factors such as visual, water, and
solid waste pollution. The lack of recreational areas and facilities
aggravates the overall evaluation.
Variation in the index values in this component as shown by Chart 13
is relatively larger than the indexes previously discussed in this
chapter since the mean index value approaches zero. This variation is
more striking at the upper portion of the bar chart than at the
bottom half. Since incomplete factors of environmental consideration
were used, no reference is made to compare the indexes in this section
to the environmental indexes derived for the large and medium group.
In general, it may be summarized that the New England and the West
North Central regions tend to demonstrate better environmental quality
than do other regions. However, the substandard SMSA's do not seem to
have any special pattern of geographic concentration. In other words
environmental quality protection for small SMSA's tends to be more of
a local than a regional problem.
HEALTH AND EDUCATION COMPONENT
The criteria used to evaluate the small SMSA's are similar to those
in the last two chapters. Due to data deficiency, the community health
conditions were, however, evaluated without two manpower factors — the
numbers of physicians and dentists per 100,000 population.
Geographically, the quality of health and education in 1970 among the
small SMSA's was found to be outstanding in most areas west of the
Mississippi River in the West North Central Region. The States of
Florida, Texas, and Utah also had two outstanding SMSA's in each.
Except Norwalk (Connecticut), there is no "A" rated SMSA east of
191
-------
Lafayette/West Lafayette (Indiana). In total, there are 17 "A"
rated SMSA's led by Columbia (Missouri) and followed by Rochester
(Minnesota) and Gainesville (Florida). Respectively, the quality of
health and education indexes for the three SMSA's are 2.79, 2.69,
and 2.65; they all exceed the mean (1.09) plus two standard deviations
(0.74).
Columbia ranked outstanding in almost all health and education cate-
gories except for health facilities which ranked 13th among the 95
SMSA's. The infant mortality rate in Columbia was 12.2 deaths per
1,000 live births, or nine deaths lower than the comparable U.S. rate.
The median school years completed in the area was 12.7, and 68.2 percent
of the persons 25 years old or over in Columbia completed 4 years of
high school or more--15.9 percentage points beyond the U.S. norm. The
hospital beds per 100,000 population in Columbia numbered 971, or
about twice as many as the U.S. average; consequently, the hospital
occupancy rate in the SMSA was 73.5 percent, or about six percentage
points lower than the U.S. average occupancy rate. Rochester ranked
second to Columbia primarily because of its lower individual educa-
tional attainment. Rochester had 5.8 percent of males 16 to 21 years
of age who were not high school graduates; the corresponding figure
for Columbia was only 4.9 percent. The percentage of population 3 to
34 years of age enrolled in school was much higher in Columbia
(64.9 percent) than in the U.S., which was 54.3 percent. This figure,
in turn,exceeded the percentage for Rochester, which was 52.2 percent.
Comparing the two outstanding areas in Florida, Gainesville and
Tallahassee, Gainesville is observed with top rankings in all subcomponent
be they health or education. Tallahassee ranked only 24th in community
medical health considerations; the ratio of hospital beds per 100,000
population was even lower than the U.S. standard. This is the basic
reason for Tallahassee's index falling to that of Gainesville's, and
it may explain, at least in part, why infant mortality rates were
higher in the former than in the latter SMSA.
The aforementioned SMSA's plus Topeka (Kansas), Lincoln (Nebraska),
Sioux Falls (South Dakota), Fargo/Moorehead (North Dakota), and other
"A" rated SMSA's in this group tend to uphold the assertion that the
health and education quality of an area is significantly influenced
by institutional effects, particularly those of state universities or
colleges.
192
-------
TABLE 14
INDEX AND RATING OF HEALTH AND EDUCATION COMPONENT (S)
149.
150.
151.
152.
153.
154.
155.
156.
157.
158.
159.
160.
161.
162.
163.
164.
165
166.
167.
168.
169.
170.
171.
172.
173.
174.
175.
176.
177.
178.
179.
180.
181.
182.
183.
ISA.
185.
186.
187.
188.
189.
190.
191.
192.
193.
194.
195.
196.
197.
198.
SHSA
Abilene, Texas
Albany. Ga.
Altoona, Pa.
Amarillo, Texas
Anderson, Ind.
Ashevllle, N.C.
Atlantic City, N.J.
Bay City, Mich.
Billings, Mont.
Blloxl-Gulfport, Miss.
Bloomlngton-Normal, 111.
Boise City, Idaho
Bristol, Conn.
Brockton, Mass.
Brownsville-Harlingen-Siin Benlto,
Texas
Cedar Rapids, Iowa
Champalgn-Urbana, 111.
Columbia, Mo.
Dsnbury, Conn.
Decatur, 111.
Dubuque, Iowa
Durham, N.C.
Fall River, Mass. -B.I.
Fargo-Moorhead, N. Dak. -Minn,
Fltchburg-Leominster, Mass.
Fort Smith, Ark.-Okla.
Cadsden, Alabama
Gainesville, Fla.
Great Falls, Mont.
Green Bay, Uls.
Jackson, Mich.
Kenosha, Vis.
La Crosse, Wls.
Lafayette, La.
Lafayette-Vest Lafayette, Ind.
Lake Charles, La.
Lavton, Okla.
Lewiston-Aubum, Maine
Lexington, Ky.
Lima, Ohio
Lincoln, Nebraska
Lubbock, Texas
Lynchburg, Va.
Manchester, B.H.
Mansfield, Ohio
HcAllen-Pharr-Edinburg, Texaa
Meriden, Conn.
Adtusted
Value
0.9167
0.7292
0.2917
1.7292
0.7292
0.8125
-0.0417
1.2083
2.0000
1.3125
1.7083
1.2500
1.2917
0.8333
0.6667
2 . 3542
1.3333
2.0000
2.7917
1.3333
0.7292
0.7917
1.5417
0.1458
2.2708
0.5833
-0.4167
-0.2500
2.6458
1 3333
1.6667
1.4583
0.8125
0.7917
2.1667
1.5833
2.2292
0.7708
0 6458
0,9792
-0.3750
1.4167
0.3750
2.1667
1.4583
0.0625
0.4583
0.4375
0.5208
0.4167
Standardized Scores
Rank
48
65
84
19
66
57
92
38
15
34
20
36
35
54
70
6
31
16
1
32
67
59
25
87
9
73
95
93
3
33
22
27
58
60
11
23
10
62
•J |
47
94
29
80
12
28
89
75
76
74
78
Rating
C
D
E
B
D
D
E
C
A
B
B
C
C
D
D
A
B
A
A
B
D
D
B
E
A
D
E
E
A
B
B
D
D
A
B
A
D
C
E
B
B
A
B
E
D
D
D
D
Standardized Scores
Value
-0.0956
-0.1903
-0.5787
0.2447
-0.2214
-0.2027
-0.9749
0.0312
0.4991
0.2282
0.4226
0.1620
0.0826
-0.2214
-0.5934
0.9632
0.0818
0.8191
1.4331
0.2043
-0.4144
-0.3087
0.4992
-0.8463
0.7081
-0.2681
-0.9202
-0.9310
1.2575
0. 1908
0.3254
0.1428
-0.2804
-0.1459
0.9199
0.3960
0.8930
-0.1868
-0.6227
-0.2287
-0.9320
0.2140
-0.4508
0.7475
0.1708
-0.7167
-0.3420
-0.3897
-0.6938
-0.5088
Bank
49
58
83
26
60
59
95
43
17
27
19
33
36
61
84
5
38
10
2
30
73
68
16
90
13
65
92
93
3
31
24
35
66
53
7
21
8
57
87
64
94
29
75
11
32
89
70
71
88
80
Rating
C
D
E
B
D
D
E
C
B
B
B
B
C
D
E
A
C
A
A
B
D
D
B
E
A
D
E
E
A
B
B
C
D
C
A
B
A
D
E
D
E
B
D
A
B
E
D
D
E
D
193
-------
TABLE 14 (Concluded)
INDEX AND RATING OF HEALTH AND EDUCATION COMPONENT (S)
_ Ad lusted Standardized Score*
SHSA Vilu» Rank Rating
199.
200.
201.
202.
203.
204.
205.
206.
207.
208.
209.
210.
211.
212.
213.
214.
215.
216.
217.
218.
219.
220.
221.
222.
223.
224.
225.
226
227.
228.
229.
230.
231.
232.
233.
234.
235.
236.
237.
238.
239.
240.
241.
242.
243.
Midland, Texas
Modesto, Calif.
Monroe, La.
Muncle, Ind.
Muskegon-Muskegon Heights, Mich,
Nashua, N.H
New Bedford, Mass.
Now Britain, Conn.
Norwatk, Conn.
Odessa, Texas
Ogdcn, Utah
Owensboro, Ky.
Petersburg-Colonial Heights, Va.
Pine Bluff, Ark.
Plttsfield, Mass.
Portland, Maine
Provo-Orem, Utah
Pueblo, Colo.
Racine, Wis.
Reno, Nev.
Roanoke, Va.
Rochester, Minn,
St. Joseph, Mo.
Salem, Oreg.
San Angelo, Texas
Savannah, Ca.
Sherman-Denison, Texas
Sioux Falls, S. Dak.
Springfield, 111.
Springfield, Mo.
Springfield, Ohio
Steubenville-Weirton, Ohlo-H. Va.
Tallahassee, Fla,
Terre Haute, Ind.
Texarkana , Texas-Ark.
Topeka, Kans.
Tuscaloosa, Alabama
Tyler, Texas
Vineland -Ml llvi lie -Bridge ton, N.J.
Waco, Texas
Waterloo, Iowa
Wheeling, W. Va.-Ohlo
Wichita Falls,. Texas
Wilmington, N.C.
1.9583
0.8750
0.7500
1.1042
1.0417
1.1458
0.0417
0.8542
2.0417
1.0208
2.4167
1.5625
0.6875
0.0208
0.7708
0.7917
2.2917
1.2083
1.0417
1.7500
1.0625
2.6875
0.3958
1.7083
1.2292
0.3125
1,3958
0.8333
2.2917
0.9167
1.2083
0.8542
0.2292
2.4583
0.7083
0.2917
2.0625
0.8333
0.9167
0.3125
0.4375
1.5417
0.3750
0.6250
0.1250
Mean
17
51
64
42
44
41
90
52
14
46
5
24
69
91
63
61
7
39
45
18
43
2
79
21
37
82
30
55
8
49
40
53
86
4
68
85
13
56
50
83
77
26
81
72
88
(i) • 1.0932
A
D
D
C
C
C
E
D
A
C
A
B
B
E
B
D
A
C
C
8
C
A
B
B
C
E
B
n
A
C
C
B
E
A
D
E
A
B
C
E
B
B
B
B
E
Standardised Scores
0.8276
0.4O42
•0.4894
•0.5767
-0.0799
-0.1682
0.9631
-0.0173
-0.0503
0.3884
0.0481
1.4524
-0.6175
0.3889
0.1487
•0.3340
0.2901
-0.0931
0.6962
-0.1802
0.0791
-0.1178
-0.3944
0.9930
-0.4289
•0.5015
0.5847
-0.2258
-0.1066
-0.4937
-0.5024
0.2163
-0.5209
-0.2221
-0.6076
Rink
18
42
67
46
39
37
91
55
12
50
9
20
76
82
47
54
6
44
45
23
41
1
86
22
34
69
25
48
14
56
40
52
72
4
74
78
15
63
51
77
79
28
81
62
85
Rating
B
C
D
C
C
C
E
D
A
C
A = Outstanding (a x + s)
B - Excellent (x + .28s £ B < X + a)
C - Good (S - .28s < C < X + .28s)
D = Adequate (x- s < D < X - .28s)
E = Substandard (< X - s)
Standard Deviation (s) « 0.7368
Mean (x) - 0.0000
Standard Deviation (s) - 0,5426
194
-------
In contrast to the "A" rated SMSA's, there are also 17 "B" rated
"excellent" SMSA's with respect to health and education quality of
life. They are touch more randomly distributed than the "outstanding"
ones. However, only 14 substandard SMSA's were revealed by Table 14.
The New England, Middle Atlantic and West South Central regions each
had three or four substandard SMSA's. Fort Smith (Arkansas-Oklahoma)
led other "E" rated SMSA's with an index as low as -0.42, or just
about 2.0 standard deviations below the mean. The index for Lewiston/
Auburn was the second lowest and Gadsden (Alabama) the third. Other
substandard SMSA's are Atlantic City, Pine Bluff, Texarkana, Altoona,
Vineland/Millville/Bridgeton, and Savannah. Except for the part of
Texarkana in Texas, this component is the only one that this state
showed "A" rated without being accompanied by "E" rated SMSA's.
It is expected that we identify those substandard SMSA's with inferior
figures in health and education comparisons with the U.S. average.
The degrees to which the figures are below the U.S. level are important
measures for decision makers to set up policy priority toward quality
of life improvements. However, it may be even more important here to
describe the good part of the quality of life among those low rating
SMSA's. For instance, Fort Smith ranked 76th in community medical
facilities; Lewiston/Auburn's best was found in individual health,
ranked 89th; Gadsden and Atlantic City even showed relative strength
in medical facilities with a ranking of 38th and 26th, respectively;
etc. Furthermore, it is extremely important to recall that this
study is motivated to make only relative comparisons rather than
absolute differentiations.
The great variation in the index values is shown in Chart 14, in which
not only the standard deviation is large (0.74), the largest devia-
tion among the five components, but the coefficient of variation is
0.68 percent, substantially higher than that for economic and political
components. The implication of this is that the health and education
needs in the small sized SMSA's vary appreciably in quality. This
quality variation is even more pronounced for the excellent and the
outstanding SMSA's than for the substandard SMSA's. Moreover, although
the variation in health and education indexes for the small and large
SMSA's is about the same, it is much greater than that for the medium
SMSA. This finding means that the need for bridging the health and
education quality gap among either the large or the small SMSA's is
likely to be more urgent than that among the medium SMSA's.
195
-------
CHART 14
REGIONAL VARIATIONS IN INDEXES:
HEALTH AND EDUCATION COMPONENT (S)
RANK SMSA
ADJUSTED STANDARDIZED SCORL
5!
1 Columbia, Mo.
2 Rochester. Minn.
3 Gainesville. Flo
4 Tallahassee. Flo.
5 Ogden. Utah
6 Bryan - College Station, Texas
7 Prove - Orem. Utah
8 Sioux Falls, S Dok
( 9 Forgo - Moorheod, N Dok , - Minn
10 Lafayette - West Lafayette. Ind.
11 Lo Crosse. Wn.
12 Lincoln, Nebr
13 Topeko, Kons
14 Norwalk. Conn
15 Billings. Mont
16 Champaign - Urbono, III
^17 Midland, Texas
f 18 Reno. Nev
19 Amanllo, Texas
20 Bloomington - Normal. 111.
21 Salem, Oreg
22 Great Falls, Mont
23 Lafayette, Lo.
24 Owensboro. Ky.
25 Durham, N C
26 Waterloo. Iowa
27 Green Bay, Wis
28 Lubbock, Texas
29 Lexington, Ky
30 Sherman - Denison, Texas
31 Cedar Rapid*. Iowa
32 Donbury, Conn
V 34 Biloxi - Gulfport, Miss
' 35 Bristol. Conn
36 Boise City, Idoho
37 San Angelo, Texas
38 Bay City, Mich
39 Pueblo, Colo
40 Springfield, Mo
41 Nashua, N H
42 Muncie, Ind
43 Roonoke, Va
44 Muskegon - Muskegon Heights
45 Racine, Wis.
46 Odessa, Texas
47 Lawton, Ok la
48 Abilene. Texas
49 Springfield, III
>. 50 Tyler, Texas
' 51 Modesto, Calif
52 New Britain, Conn.
53 Springfield, Ohio
54 Brockton, Moss
55 Sioux City, Iowa - Nebr.
56 Tuicoloosa. Ala
57 Asheville, N C
58 Jackson, Mich
59 Dubuque. Iowa
60 Kenosha. Wis
61 Portland, Maine
62 Lake Charles, Lo.
63 Pittsfield, Moss
64 Monroe. Lo
7 65 Albany, Go
\ 66 Anderson, Ind
67 Decotur. Ill
68 Terre Haute. Ind
69 Petersburg - Colonial Heights
70 Brownsville - Harlingen - Son
71 Laredo, Texas
72 Wichita Falls. Texas
73 Fitchburg - Leominster, Moss
74 McAllen- Phorr - Edlnburg,
75 Manchester, N.H
76 Monsf.eld, Ohio
77 Waco, Texas
78 Meriden, Conn.
79 St Joseph. Mo
80 Lima, Ohio
k 81 Wheeling. W Vo - Ohio
" 82 Savannah, Ga
84 Altoono, Po
85 Texarkano. Texos - Ark
86 Steubenv.lle - Weirton, Ohio
87 Fall River. Moil - R 1
I 88 Wilmington, N.C.
\ 89 Lynchburg. Va
90 New Bedlord, Moss
91 Pine Bluff. Ark.
92 Atlantic City, N J
93 Godsden. Alo.
94 Lewiston - Auburn, Mo, ne
t 95 Fort Smith, Ark. - Oklo.
. Mich
Va
Benito. Texas
exas
- W Vo MM
s ;
9
X
»
I
•1 Qfi
•I--'D
x-s
X "Mean = 1.0932
S « Standard Deviation = 0.7368
-------
CO
bO
C
•r4
4J
ns
- o
4-1
c c
o cu
•H C
4-> O
3 a
& e
••-I O
co C
•H o
a -H
4-J
O CO
•H O
,d 3
CX T3
CO W
M
60 T3
O C
01 cfl
O
I 4-1
T—I
^^
01
4J
CO
3
cr
01
13
-------
SOCIAL COMPONENT
Except for one factor in the community living conditions—the number
of banks and savings and loan associations per 1,000 population, for
which statistical data were not available—all factors used to assess
the social quality of life in the large and medium SMSA's were re-
tained in the measurement of the social component for the small SMSA's.
Since more than 50 variable factors are included, one missing factor
should not make a significant change in the overall evaluation.
Thus, the resulting findings in this section are comparable on a rela-
tive basis to those for the social component in the preceding chapters.
The number of small SMSA's with outstanding social quality of life is
relatively smaller than is the case with the other components such as
political, environmental, and health and education. Only 13 SMSA's
had index values exceeding the mean (0.50) plus one standard deviation
(0.35), and hence, denoted as "A" or "outstanding." La Crosse, the
small SMSA which led other outstanding SMSA's in political quality,
also leads in the social component. It received an index of value
1.47 or about 2.8 standard deviations above the mean. As shown in
Table 15, the index for La Crosse appreciably exceeds that for
Rochester, the second highest in the group. The second runner-up
is Lincoln which also scored "A" in the economic and health and educa-
tion component. Slightly behind Lincoln in score are Green Bay and
Topeka, both with excellent or outstanding records in other quality
of life components under discussion. The remaining "A" regions are
Billings, Sioux Falls, Reno, Fargo/Moorhead, Manchester, St. Joseph
(Missouri), Provo/Orem (Utah), and Lewiston/Auburn. It is significant
to note from Figure 15 that with the exception of two in New England,
no SMSA south of Topeka and east of Green Bay was rated outstanding
in the social component.
Of special interest is that the northern part of the State of Texas,
which was strong in the economic and health comparisons, was consider-
ably lower in the political and social quality assessments. Two
southern SMSA's in the state, McAllen/Pharr/Edinburg and Brownsville/
Harlingen/San Benito, which had been rated substandard in both the
economic and political components, again rated as "substandard" in the
social quality of life evaluation. Those two SMSA's showed very good
ratings in the individual quality category, especially in the area
of racial discrimination. Nevertheless, the areas were substantially
inadequate in providing good community living conditions, in general,
and social conditions in particular. Due primarily to the weak
198
-------
TABLE 15
INDEX AND RATING OF SOCIAL COMPONENT (S)
149.
150.
151,
152.
153.
154.
155.
156.
157.
158.
159.
160.
161.
162.
163.
164.
165.
166.
167.
168.
169.
170.
171.
172.
173.
174.
175.
176.
177.
178.
179.
180.
181.
182.
183.
184.
185.
186.
187.
188.
189.
190.
191.
192.
193.
194.
195.
196.
1S7.
198.
Abilene, Texas
Albany, Georgia
Altoona, Pennsylvania
Amarlllo, Texas
Anderson, Indiana
Asheville, North Carolina
Atlantic City, New Jersey
Bay City, Michigan
Billings, Montana
Boise City, Idaho
Bristol, Connecticut
Brockton, Massachusetts
Brovnsvl1le-Harlin]
Bryan-College Stat:
Cedar Rapids, Iowa
Champalgn-Urbana, Illinois
Columbia, Missouri
Danbury, Connecticut
Decatur, Illinois
Dubuque, Iowa
Durham, North Carolina
Fall River, Mass
Fargo-Maorhead,
Fitchburg-Leonin
Fort Smith, Arka1
Gadsden, Alabama
Gainesville, Florida
Great Falls, Montana
Green Bay, Wisconsl1
Jackson, Michigan
Kenosha, Wisconsin
La Crosse, Wisconsin
Lafayette, Louisiana
Lafayette-West Lafayi
Lake Charles, Louisiana
Laredo, Texas
Lawton, Oklahoma
Lewlston-Auburn, Maine
Lexington, Kentucky
Lima, Ohio
Lincoln, Nebraska
Lubbock, Texas
Lynchburg, Virginia
Manchester, New Hampshire
Mansfield, Ohio
McAllen-Fharr-Edinburg
Meriden, Connecticut
Adjusted
SHSA Value
0.5198
0.1927
la 0.4158
0.7387
0.2506
rolina 0.2266
Jersey 0.0448
0.3497
1.0761
sslsaippi 0.2225
Illinois 0.8250
0.7689
it 0.7228
etts 0.4370
:en-San Benlto, Texas 0.1202
on, Texas 0.2265
O.J359
llinois 0.5211
0.7782
t 0.7511
0.6225
0.7862
ina 0.5900
lUsetts-Rhode Island 0.1497
•th Dakota-Massachusetts 1.0028
•r, Massachusetts 0.6858
..-Oklahoma -0.2266
0.0363
:a 0.5839
y, Texas 0.3493
.a 0.7300
n 1.1032
0.4329
0.3637
.n 1.4668
la 0.2263
yette. Indiana 0.6378
dana 0.3063
0.2451
0.4396
line 0.8716
0.3373
0.2131
1.1356
0.5378
, -0.0461
ipshlre 0.9797
0.3511
iurg, Texas 0.0489
it 0.4795
Standardized Scores
Rank
47
77
55
25
68
72
87
62
6
75
14
22
28
50
«4
73
43
46
20
24
36
19
38
79
9
30
95
88
39
63
27
4
52
59
1
74
34
65
69
49
13
64
76
3
42
91
10
61
86
48
Rating
C
0
C
B
D
D
E
D
A
D
B
B
B
C
E
D
C
C
B
B
B
B
C
E
A
B
£
E
C
D
B
A
C
D
A
D
B
D
D
C
A
D
D
A
C
E
A
D
E
C
Standardized Scores
Value
0.0866
-0.2301
-0.0222
0.0372
-0.1215
-0.1369
-0.3424
-0. 1663
0.3768
-0.1384
0.2796
0.0785
0.2509
-0.1029
-0.4890
-0.1823
0.0753
0.0020
0.3279
0.2495
0.0961
0.1927
0.1095
-0.2950
0.4659
0.1247
-0.5033
-0.2621
0.1241
-0.1182
0.1589
0.4518
-0.0702
-0.0613
0.7014
-0.1228
0.1765
-0.0322
-0.5677
-0.0820
0.2592
-0.0564
-0.2676
0.4160
0.0412
-0.4206
0.3530
-0.1057
-0.6721
-0.0748
Rank
37
77
47
43
65
69
85
74
9
70
15
38
17
61
91
75
39
44
12
18
35
22
34
83
3
31
92
80
32
64
28
5
55
53
1
66
26
48
93
58
16
52
81
8
42
90
11
62
95
56
Hating
B
D
C
C
D
D
E
D
A
D
A
B
B
D
E
D
C
C
A
t
e
t
t
i
A
B
E
D
B
D
B
A
C
C
A
D
B
C
E
D
B
C
D
A
C
E
A
D
E
C
199
-------
TABLE 10 (Concluded)
INDEX AND RATING OF SOCIAL COMPONENT (S)
Adjusted 3tandardt««d Scoro
StandardUsd Scores
199. Midland, T*XM
200. Modwto, California
201. Monroe, Louisiana
202. Munclc, Indiana
203. Muskegon-Muskegon Heights, Michigan
20A. Nashua, New Hampshire
205. New Bedford, Massachusetts
206. Hew Britain, Connecticut
207. Norwalk, Connecticut
208. Odessa, Texas
209. Ogden, Utah
210. Ovensboro, Kentucky
211. Petersburgh-Colonlal teights, Virginia
212. Pine Bluff, Arkansas
213. Pittsfield, Massachusetts
214. Portland, Maine
215. Provo-Orem, Utah
216. Pueblo, Colorado
217. Racine, Wisconsin
218. Reno, Nevada
219. Roanoke, Virginia
220. Rochester, Minnesota
221. St. Joseph, Missouri
222. Salem, Oregon
223. San Angelo, Texas
224. Savannah, Georgia
225. Sherman-Denison, Texas
226. Sioux City, Iowa-Nebraska
227. Sioux Falls, South Dakota
228. Springfield, Illinois
229. Springfield, Missouri
230. Springfield, Ohio
231. Steubenvllle-Weirton, Ohio-West Virginia
232. Tallahassee, Florida
233. Terre Haute, Indiana
234. Texarkana, Texas-Arkansas
235. Topeka, Kansas
236. Tuscaloosa, Alabama
237. Tyler, Texas
238. Vlneland-Millville-Bridgeton, New Jersey
239. Waco, Texas
240. Waterloo, Iowa
241. Wheeling, West Virginia-Ohio
242. Wichita Falls, Texas
243. Wilmington, North Carolina
V.lu.
0.6024
0.1461
0.2938
0.2443
0.4360
0.5257
0.0599
0.6735
0.8007
0.7754
0.6713
0.2863
0.1233
-0.1229
0.8211
0.6884
0.8749
0.5784
0.3585
1.0046
0.4196
1.2354
0.8899
0.4244
0.8204
0.1233
0.5271
0.6545
1.0083
0.7625
0.7363
0.1460
0.0194
0.5683
0.3948
-0.2097
1.1026
-0.0177
0.4105
0.2427
0.3823
0.8065
0.1664
0.6269
-0.1506
JUnk
37
80
66
70
51
45
85
31
18
21
32
67
83
92
15
29
12
40
60
8
54
2
11
53
16
82
44
33
7
23
26
81
89
41
57
94
5
90
56
71
58
17
78
35
93
lUtlng
B
E
D
0
C
C
E
B
B
B
B
D
E
E
B
B
A
C
0
A
C
A
A
C
B
E
C
B
A
B
B
E
E
C
D
E
A
E
C
D
0
B
D
B
E
Msa
-0.0443
-0.2565
-0.0906
-0, 1333
-0.0388
-0.0671
-0.3070
0.0653
0.1664
0.2837
0.1255
-0.1311
-0.3797
-0.4031
0.2926
-0.0815
0.4529
0.1159
-0.0395
0.4311
-0.1116
0.6810
0.1905
-0.0214
0.2406
-0.2358
0.0539
0.1280
0.3563
0.1913
0. 1924
-0 1877
-0.2949
0.0898
-0.1014
-0.4037
0.4422
-0.3521
-0.1559
-0.1449
-0.0001
0.2455
-0.1530
0.1968
-0.5979
tet
11
79
59
68
49
54
84
40
27
14
30
67
87
88
13
57
4
33
50
7
63
2
25
46
20
78
41
29
10
24
23
76
82
36
60
89
6
86
73
71
45
19
72
21
94
Uttnl
C
0
D
D
C
C
E
C
B
A
B
D
E
E
A
0
A
B
C
A
D
A
B
C
B
0
C
B
A
B
B
D
E
B
D
E
A
E
D
D
C
B
D
B
E
M»n (S) - 0.4957
Standard Deviation (s) - 0.3451
Mean (x) - 0.0000
Standard Deviation (s) - 0.2742
A - Outstanding (£$+«)
B - Excellent (x + .28s £ B < 9t + s)
C " Good (x - .28s < C < x + .28s)
D - Adequate (ie - s <: D s x - ,28s)
E " Substandard (4 x - s)
200
-------
co
bO
C
•H
4->
cti
PS
M-l
O
c
o
•r-l JJ
s-i c
4J CU
CO C
•H O
P CX
o o
•.H U
p- 1-1
a to
M -H
60 O
o o
-------
economic conditions, residents in these areas were short of existing
opportunities for self-support and for independence,,
The lowest city in social quality comparison is Fort Smith, which ob-
tained a negative index of -0.23 or about 2.1 standard deviations below
the mean. Texarkana was found to have the second lowest index of
-0.21. The other four with negative indexes are Wilmington (North
Carolina), Pine Bluff, Lynchburg, and Tuscaloosa. The negative indexes
resulted from extremely high values of factors which have adverse
effects upon the social quality of life. For instance, the high popu-
lation density and the high percentage of population under 5 and over
65 years of age living in the central city are considered negative
inputs in spatial extention related to individuals' choice; all kinds
of discrimination--racial, sex, and spatial--the crowdedness in living
space, the high rates of death, birth, and crimes are also undesirable
social factors which tend to lower our quality of life. Therefore,
if the negative input factors in any area are sufficiently strong to
more than compensate for the positive factors, the area's overall quality
of life index becomes negative. The aforesaid SMSA's are examples
of the extremes. For instance, Fort Smith ranked last in spatial
inequality in that it had very high housing segregation and income
inequality indexes; they all are three times the U.S. average, and more
than one-fifth of its residents had to work outside of the county of
residence; Texarkana ranked very low in the provision for decent
community living conditions because of its high percentage of occupied
housing, with 1.01 or more persons per room, and high crime and death
rates; the sex inequality in Wilmington and the crowded living space
in Pine Bluff were problem areas in those SMSA's.
In addition to those just mentioned, there are 11 additional SMSA's
rated substandard. They are scattered in the eastern and southern
regions. Among them, six SMSA's had index values only barely exceeding
the threshold of the mean minus one standard deviation. In order of
rankings, they are Brownsville/Harlingen/San Benito, Petersburg/
Colonial Heights (Virginia), Savannah, Springfield (Ohio), Modesto
(California), and Fall River (Massachusetts-Rhode Island).
Modesto is the only small SMSA along the West Coast where only one
substandard rating was given among all five quality of life components.
Its index is 0.15 and ranked 80th in the group. The major causes for
this SMSA to fall into the "E" category are its high racial inequality
indexes and low rating of self-supporting opportunities--its labor
force participation rate in 1969 was only 63.2 percent or 2.8 percentage
points below the U.S. level. The mean income per family member
202
-------
RANK
CHART 15
REGIONAL VARIATIONS IN INDEXES:
SOCIAL COMPONENT (Sj
SMSA ADJUSTED STANDARIZEP SCORE
S-S
B <
D
I LoCrosse. Wii
2 Rochester, Minn
3 Lincoln. N.br
4 Green Bay, Wit
5 Topeko, Konias
6 Billings, Mont
7 Sioux Folli, SDok
8 Reno, Nev
9 Forgo - Moorhead. N Dak - Minn
10 Monche.ter. NH
II St. Joseph, Mo
12 Provo - Orcm, Utah
13 Lewitton - Auburn, Maine
14 Bloofnington - Normal. Ill
15 Pittsfield. Moss
16 Son Angelo, Texat
17 Waterloo, Iowa
18 Norwolk, Conn
19 Dubuque. Iowa
20 Colombia. Mo
21 Odetio. Texas
22 Boise City. Idaho
23 Springfield. Ill
24 Danbury, Conn
25 Amarillo. Texas
26 Springfield. Mo
27 Great Folli, Mont
28 Bristol, Conn
29 Portland, Maine
30 Fitchburg - Leominsrer, Mass
31 New Britain, Conn
32 Ogden, Utah
33 Sioux City, lowo - Nebr
34 Lafayette - West Lafayette, Ind
35 Wichita Falls, Texas
36 Decatur. Ill
,37 Midland. Texas
'38 Durham. NC
39 Gainesville. Fla
40 Pueblo. Calo
41 Tallahassee. Fla
42 Lubbock, Texas
43 Cedar Rapids, lowo
44 Sherman - Denison, Texas
45 Nashua. NH
46 Champaign - Urbona, III
47 Abilene. Texas
48 Meriden, Conn
49 Lawton, Oklo
50 Brockton, Mass
51 Muckegan-Muskegon Heights, Mich
52 Jackson, Mich
53 Salem, Oreg
54 Roonoke, Vo
55 Altoona. Pa
56 Tyler. Texas
57 Terre Haute. Ind
58 Waco, Texas
59 Kenosho, Wis
60 Racine. Wis
61 Mansfield. Ohio
62 Boy City. Mich
63 Golveston - Texas City, Texas
64 Lexington, Ky
65 Lake Charles, La
66 Monroe. La
67 CWnsboro, Ky
68 Anderson, !nd
69 Laredo, Texas
70 Muncie, Ind
71 Vinelond - Millville - Bndgeton, N.J.
72 Asheville, NC
73 Bryon - College Station, Texas
74 Lafayette, La
75 Biloxi - Gulfport, Miss
76 Lima, Ohio
77 Albany, Go
78 Wheeling. WVa - Ohio
fTi Fall River. Moss - Rl
80 Modesto. Calif
81 Springfield, Ohio
82 Savannah, Ga
83 Petersburg - Colonial Heights, Va
84 Brownsville -Horlingen- Son Benito, Texa
85 New Bedford. Mass
86 McAllen - Pharr - Edinburg, Texas
87 Atlontic Cily. NJ
88 Gadsden. Ala
89 Steubenvill.-WeirtoJ.. OMo-WVo
90 Tuscaloosa. Ala
91 Lynchburg. Vo
92 Pine Bluff, Aik
93 Wilmington. NC
94 Texarkana, Texas - Ark
,95 Fort Smith. Ark - Okla
X+s
203
5!-Mean - .4957
S " Standard Deviation
.3451
-------
amounted to only $2,886 or more than $200 below the U.S. average; the
Negro to total population professional employment adjusted for educa-
tion was only one-seventh the U.S. ratio, and the Negro males to total
males unemployment rate was twice as high as the U.S. situation, etc.
Among all the excellent and outstanding SMSA's in the New England
region, Fall River and New Bedford in Massachusetts, next to each
other near the coast, were the two substandard areas. While the lack
of mobility, information, and spatial extention were identified as
the serious individual concerns in both areas, Fall River
experienced very little racial inequality and New Bedford had little
sex discrimination.
Again, the high ranking SMSA's have areas of weakness. To perfect
its social quality of life, La Crosse should, as diagnosed by this
study, attempt to increase its opportunities for individual self-
support and reduce racial inequality in employment and earnings. For
Rochester, the urgent need is to improve its general community living
conditions by reducing the high crime rates, which significantly
dragged the rank of Rochester to below the average in this sub-
component. Lincoln was rated very low in employment: and earning
equality between races and between the sexes. Green Bay faces in-
equality problems between sexes, and Topeka was unfavorably evaluated
in the area of racial inequality. Similar diagnoses on social quality
of life for all small SMSA's can be undertaken and areas of potential
weakness can be identified accordingly.
The preceding two paragraphs once again attempt to pinpoint examples
of weaknesses in social factors affecting the quality of life in both
the outstanding and the substandard SMSA's. Clearly, no region has
the best or perfect quality of life--there are always areas which
deserve further enrichment and betterment.
The dispersion of the indexes in this component is unexpectedly small;
the standard deviation of 0.35 is lower than any comparable figures in
other quality of life components in this small size group. The coef-
ficient of variation, which measures the differences among index values,
however, is relatively high, 0.70, or higher than any coefficients
obtained previously in this chapter. The implication of this is that
the geographic variation in ratings among the small SMSA's in this
country is still very much undesirable. Essentially, how to reduce
the geographic differentials in social quality of life among regions
becomes a major concern of public agencies if an ultimate objective
is to guarantee a high quality of social life for all. urban population
regardless of location.
204
-------
SUMMARY AND CONCLUSIONS
Generally, the quality of life assessments for the small SMSA's reveal
no stronger pattern of regional concentration of the various quality
of life ratings than those observed in the preceding two chapters for
the large and medium SMSA's. However, most discussions in this chapter
have centered around the East North Central and New England regions
and the State of Texas because these areas contain a large number of
small SMSA's.
Relatively excellent and outstanding ratings for the economic component
were observed in the East North Central region and the northern part
of the State of Texas. The three southern SMSA's in Texas and the
southeastern states include a large proportion of the substandard
SMSA's. The dispersion of the economic component indexes for the
small SMSA's is larger than those for the large and medium SMSA's, as
is the coefficient of variation. This indicates that the disparity in
terms of economic quality of life among small SMSA's is larger than
that among the large or the medium SMSA's. In other words, should
there be regional inequality between economic well-being among people
in the U.S., it is more so among the small than among the large or
medium metropolitan areas.
The strong geographic concentration pattern of political ratings dis-
closed for the large and medium SMSA's was repeated here for the small
SMSA's. The quality of life in terms of political concerns was found
co be superior in the northern part of this country to those in the
southern part of the U.S. The small SMSA's in the New England region
and the Mountain states were outstanding with respect to political
quality. In spite of regional differentials in political ratings, the
index values in this group result in a small variation with the coef-
ficient of variation being 0.25. The small coefficient indicates
that, as far as political considerations are concerned, people among
the small SMSA's do not experience significant deviations in quality
of life even though the relative patterns between north and south
prevailed and were persistent for the three size groups.
Due to the lack of air quality and climatological data, the environ-
mental component in this chapter was evaluated only with the remaining
pollution factors and the parks and recreational data. Thus, geo-
graphic comparisons on patterns of environmental rating distribution
between the large, medium, and small group of SMSA's are not appro-
priate. Probably because of this data limitation, the environmental
205
-------
quality evaluation for the small SMSA's indicates in general very little
regional pattern in the ratings. The SMSA's in the New England region
nevertheless, did show off outstandingly. The environmental ratings
for the two small SMSA's on the West Coast did support the pattern of
high environmental quality found in the large and medium SMSA's on
the West Coast.
The quality of health and education among the populations in various small
SMSA's tended to depict more or less a random regional distribution,
although the West North Central, the Mountain regions, and the Pacific regi
seem to be differentiated from the rest. A large standard deviation
and high coefficient of variation for the health and education com-
ponent indicates that regional differences in health and educational
quality are appreciable. In addition, the influence of institutions,
especially the leading state universities and colleges, on regional
quality of health and education was strikingly evident in the small
SMSA's.
Among quality of life components in both the large and medium groups,
the clearest patterns of regional distribution among quality ratings
were found in the social concerns. The social component ratings in the
small group tend to confirm the existence of this regional differen-
tiation. Almost all SMSA's in the Pacific region plus those to the north
of Wichita (Kansas) and west of Ann Arbor (Michigan) were rated out-
standing. Except a few in New England and one in Florida, none of the
remaining SMSA's received the "A" rating. In contrast, almost all UE"
rated medium and small SMSA's were found in the southeastern states.
Among the small SMSA's, the quality ratings for the social component
are highly correlated geographically to those for health and education
and to a lesser degree to those for the economic component. The co-
efficient of variation among index values for the social component is
0.70, or the highest among the five quality of life components in the
small SMSA's. This indicates wide variations in the social quality of
life enjoyed by people in different urban areas in the U.S. Specifi-
cally, it reflects a need for both public and private efforts to pro-
vide an acceptable level of social quality of life for the substandard
regions. There is clearly a need for further investigation into the
regional inequalities in social concerns and the courses of action that
can be launched to remove the deep-rooted factors adversely affecting
our social quality of life in the concentrated substandard regions.
206
-------
Except for the environmental component, the rankings produced by the two
methods are also very consistent for the small group of SMSA's, with the
rank-order correlation coefficient being greater than 0.95 for the four
quality of life components. For the environmental component, the
coefficient is 0.82.
207
-------
CHAPTER VIII
SUMMARY AND CONCLUSIONS
The practical importance of social indicators has been recognized since
the publication of the first census, conducted for purposes of taxation
or to determine potential military strength. In fact, many people
in this country would admit that the leading role played by the
U.S. in the world economy after the Depression can be partially attri-
buted to the establishment of a system of economic indicators which
have been constantly relied on to evaluate our economic performance
and to help guide our economy.— The ideal to be sought in this
country is not a planned society but a continuously planning society
in which integration and equilibrium are produced by groups and in-
dividuals undergoing a continual process of reviewing the past,
adjusting the present and planning for the future. Our ability to
evaluate what we have done, and to plan ahead, is dependent on our
ability to assess how we are relative to how we were. To enhance
the ability, the President's Science Advisory Committee in 1962, called
for the systematic collection of basic behavioral data for the United
States—the data that are comparable, systematic and periodically
gathered, organized, and analyzed.—
Last year, Social Indicators 1973 was published. It is "a book of
statistics, the first of its kind to be published by the Federal
Government. It contains a collection of statistics selected and or-
ganized to describe social conditions and trends in the United
States."— The major criticisms of this book of statistics are the
lack of interpretative text, the concentration on output measures,
I/ For instance, see Raymond Bauer, "Social Indicators and Sample
Surveys," in Public Opinion Quarterly. Vol. 30, No. 3 (Fall 1966,
pp. 339-352).
2f President's Science Advisory Committee, Strengthening the Behavioral
Sciences: Statement by the Behavioral Science Subpanel (Washing-
ton D.C., April 20, 1962).
3f Daniel Tunstall, Social Indicators 1973 (Washington, D.C.: Office
of Management and Budget, 1974).
208
-------
and the ambiguity among objectives--whether for goal setting or for
policy implementation, for government or for general public informa-
tion.^/
This present study provides not only a set of comprehensive economic,
political, environmental, health and education, and social quality of
life indicators for all 243 SMSA's in the U.S., but also a theoretical
framework in which the interwoven relationships among individuals and
the institutions in the community can be objectively measured, evalu-
ated and analyzed. The ultimate objective of this study is, naturally,
to stimulate actions toward the improvement of the overall quality of
life for all people. The report represents a first step by identifying
potential weaknesses and strengths for all the metropolitan areas in
this country.
An economic production model has been developed in this study. The
quality of life for any individual is conceptually viewed in the model
as an output produced by variable combinations of both psychological
and physical inputs that the individual can normally exchange with,
or acquire from, others in his community. Therefore, the quality of
life that each individual perceives is assumed to be directly depen-
dent on his capability constraints to exchange and to acquire, which
vary from place to place and from time to time. For policy decision
makers who attempt to maximize the quality of life output for all con-
stituents collectively, however, the major concern is how to improve
an individual's capability by shifting the constraint curve outward to
the right.
To measure objectively the output level of quality of life as sub-
jectively perceived by an individual, we may start with the input
measures, since the optimum level of quality of life is produced only
by combining both the physical and psychological inputs in such forms
as to locate the tangency point between the iso-quality and the
capability constraint curves. Without an extensive survey of attitudes
among the individuals under study, it is very difficult for anyone
even to attempt to quantify, much less to actually measure, the number
of psychological inputs employed in the quality of life production.
4/ For various critics, see Roxann Van Dusen, "Problems of Measure-
ment in Areas of Social Concerns," Monthly Labor Review
(September 1974), pp. 7 and 8; and Richard Taeuber, "Social In-
dicators and Policy Making," Proceedings of the American
Statistical Association, Social Statistics Section (1974).
209
-------
Nevertheless, it is much less difficult to attempt to measure the
physical inputs used in the quality of life production if we assume
that the psychological inputs are constant over time. Although it is
more complicated to measure the physical input for a community than
for a person at a particular point in time, the quality of life out-
put measured for a community by this particular approach tends to be
more informative and reliable than for any individual because of the
collective nature and the common law of large numbers. In addition,
the assumption of constant psychological input for a community on the
whole is more realistic and less rejectable than for any individual.
In social statistics, as in economic and political statistics, atten-
tion has been traditionally focused on the state of the nation as a
whole. Although it is very important to have the aggregate national
statistics such as the Gross National Product for national policy and
decision making, the aggregate statistics and national averages fail
to reveal the regional and local situations, and hence, overlook the
extremes. Yet regional variations in social, economic, political, and
environmental conditions are critical issues of our national problems
today. For instance, regional migration has been found to be more
responsive to the quality of life indicators than to the conventionally
assumed determinant--income or employment.—
Based on the preceding rationale and in full awareness of the mounting
needs for the social indicators with which to determine priority, define
targets, and assess performance, this metropolitan quality of life com-
parison study was originated. The quality of life indexes that this
study developed for the Standard Statistical Metropolitan Areas (SMSA's)
actually represent physical input indicators in these areas. The
variations among the indexes so constructed may reflect the quality of
life variations only by assuming a constant level of psychological in-
puts throughout the SMSA's in the country. Interpretations of the re-
sults shown in the study have to be given with care, and the users of
this study are urged to be fully aware of the weakness and limitations
of this type of descriptive analysis, and the definitions, methodology,
and data sources used.
See Ben-Chieh Liu, "Net Migration Rate and the Quality of Life,"
Review of Economics and Statistics, Vol. 57, No.3 (August 1974),
210
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Incorporating some 123 factors and variables which are of substantial
influence upon the objective quality of life or can most represent
the physical inputs to the production of the basic quality of life,
the level of quality of life in the 243 SMSA's in this country in 1970
was measured through the five different quality of life components--
economic, political, environmental, health and education, and social.
The economic component consists of factors representing individual
economic well-being as well as community economic health. The politi-
cal component consists of variables relating to individual politi-
cal activities, local government professionalism and performance, and
welfare assistance. The environmental component comprises quality
measures of all types of pollution (air, water, noise, visual, and
solid waste) and natural environment (climatological data and parks,
trails, and recreational areas). The health and education component
includes indicators of individual health and education attainment, and
community educational investment and medical care provision. The social
component encompasses the ratings of individual equality and individual
concerns plus the level of community living conditions.
The 243 SMSA's were divided into three groups--large, medium, and small.
According to the 1970 population, there are 65 large SMSA's with a popu-
lation over 500,000, 83 medium SMSA's (200,000 to 500,000), and 95 small
SMSA's with population less than 200,000. Based on 1970 data, the
composite indexes were developed and constructed for the five quality of
life components for each of the 243 SMSA's individually.
The composite indexes were constructed on the basis of the group means ,
and hence,are in relative terms. The value of the composite indexes
for any special component is of importance only relative to its group
mean value. The relative composite indexes are meaningful only when
cotrparisons are made among members within the same group. Intergroup
comparisons should be interpreted with caution. Bearing in mind those
characteristics of the composite indexes, the indexes themselves are
then considered as cardinal rather than ordinal. In other words, if an
SMSA has an index two times as large as that for another SMSA in the
same group for the same component, the quality of life in the former
SMSA may be interpreted as twice as good as that in the latter SMSA.
However, since the index value depends entirely upon the structure of
the model and the factor weights expressed in the model, it is safe to
consider the composite indexes as ordinal. Given the indexes and the
means (x) and standard deviations (s) of the indexes in the same group,
the quality of life of the SMSA's were then identified to be either out-
standing (A), excellent (B), good (C), adequate (D), or substandard (E).
The empirical findings of the quality of life enjoyed by residents in
different SMSA's by the quality of life component are summarized in the
following tables.
211
-------
TABLE 16
QUALITY OF LIFE INDEXES AND RATINGS IN LARGE SMSA'S
Economic
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
SMSA
Akron, Ohio
Albany-Schenectady-
Troy, N.Y.
Allentown-Bethlehem-
Easton, Pa.-N.J.
Anaheim-Santa Ana-
Garden Grove, Ca.
Atlanta, Ca.
Baltimore, Md.
Birmingham, Ala.
Boston, Mass.
Buffalo, N.Y.
Chicago, 111.
Cincinnati, Ohlo-Ky.-
Ind.
Cleveland, Ohio
Columbus, Ohio
Dallas, Texas
Dayton, Ohio
Denver, Colo,
Detroit, Mich.
Fort Lauderdale-
Hollywood, Fla.
Fort Worth, Texas
Gary -Hammond -East
Chicago, Ind.
Grand Rapids, Mich.
Greensboro-Winston-
Sa lea-High Point, N.C.
Hartford, Conn.
Honolulu, Hawaii
Houston, Texas
Indianapolis, Ind.
Jacksonville, Fla.
Jersey City, H.J.
Kansas City, Mo.-Ks.
Los Angeles-Long
'Beach, Ca.
Louisville, icy. -Ind.
Memphis, Tenn.-Ark.
Miami, Fla.
Milwaukee, Wls.
Mlnnespolls-St . Paul,
Minn.
Nashvi lie -Davidson ,
Tenn.
New Orleans, La.
New York, N.Y.
Newark, N.J.
Norfolk-Portsmouth, Va.
Value
1.8786
1.3286
1.4286
2.1786
2.4714
1.3429
1.0500
1.1786
1.8357
2.3643
2.3429
2.5143
1.7857
2.7571
2.1214
1.8357
1.8929
2.3143
2.4786
1.3929
2.2643
1.1571
2.0357
1.1357
2.7000
2.5143
0.8929
0.5857
1.6857
2.0500
1.9071
0.9429
1.2857
2.1786
1.9357
1.7286
0.7857
1.9500
1.2571
0.8500
Rating
C
D
D
B
A
D
E
E
C
A
A
A
C
A
B
C
B
A
A
D
B
E
B
E
A
A
E
E
C
B
£
E
D
B
B
C
E
B
D
E
Political
Value
2.6319
3.7431
2.4792
3.0486
1.8750
2.5278
1.6944
3.3889
3.8819
2.9653
2.8403
2.7847
3.0208
1.4653
2.5625
3.0903
3.2222
2.1319
1.7986
2.2778
3.6319
1.8333
3.6181
2.1458
1.9167
2.4236
1.7569
2.1250
2.0486
2.5278
2.3403
1.8264
1.9097
3.2708
3.4722
2.0833
1.5625
2.2014
2.9931
1.9306
Rating
C
A
C
B
E
C
E
A
A
B
B
C
8
E
C
B
B
D
E
D
A
E
A
D
E
D
E
D
D
C
D
E
E
A
A
D
E
D
B
E
Environ
Value
-0.9667
-1.2917
-0.6167
-1.0500
-1.2833
-1.2667
-1.4250
-1.2500
-1.2000
-1.8167
-1.0333
-1.4250
-1.0917
-0.9083
-1.3167
-0.9917
-1.7250
-1.0833
-0.8583
-1.1750
-1.0333
-1.3000
-1.1250
-0.4583
-1.0000
-1.5250
-1.2500
-1.0167
-1.1250
-1.0583
-1.4167
-1.2083
-0.4167
-1.0417
-0.9000
-1.0833
-1.2667
-1.3333
-1.2000
-0.8667
mental
Rating
C
D
A
C
D
D
E
D
D
E
C
E
C
B
D
C
E
C
B
D
C
D
C
A
C
E
D
C
C
C
E
D
A
C
B
C
D
D
D
B
Health and
Value
1.1250
1.8625
0.3875
2.0125
0.8375
0.3625
-0.0250
2.0125
1.4250
0.6625
0.6250
1.0875
1.4875
0.7625
1.0625
2.5000
0.9625
0.2000
0.3500
0.7000
1.5375
0.1000
2.2750
1.5375
1.0875
0.6500
0.1125
-0.5250
1.1125
1.7375
0.3125
0.6125
0.6000
1.70OO
2.2375
0.6375
0.4250
1.2125
1.2625
0.0625
Education
Rating
C
B
D
A
D
D
E
A
B
D
D
C
B
D
C
A
C
E
D
D
B
E
A
B
C
D
E
E
C
B
E
D
D
B
A
D
D
C
C
E
Social
Value
0.1835
0.5836
0.2173
0.4762
0.2806
0.1392
0.0931
0.6036
0.7019
0.3056
0.0711
0.5837
0.7621
0.4585
0.3421
0.9604
-0.0248
0.5823
0.4372
0.2106
0.5527
0.2337
0.5981
0.4496
0.5573
0.4303
0.3169
-0.1694
0.8089
0.8315
0.2603
0.1198
0.7634
0.8453
0.8329
0.7218
0.1783
0.5179
0.1000
0.2507
Rating
E
B
D
C
D
E
E
B
B
D
E
B
B
C
D
A
E
B
C
D
C
D
B
C
C
C
D
E
A
A
D
E
B
A
A
B
E
C
E
D
Overall
value
0.9705
1.2452
.7792
1.3332
.8362
.6211
.2775
1.1867
1.3289
.8962
.9692
1 . 1090
1.1929
.9070
.9544
1.4789
.8656
.8290
.8412
.6813
1.3906
.4048
1.4804
.9621
1.0523
.8986
.3658
.1999
.9061
1.2177
.6807
.4587
.8284
1.3906
1.5157
.8176
.3370
.9097
.8825
.4454
Rating
C
B
D
B
D
D
E
B
B
C
C
B
B
C
C
A
D
D
D
D
A
E
A
C
C
C
E
E
C
B
D
E
D
A
A
D
E
C
D
E
212
-------
TABLE 16 (Concluded)
Economic
SHS\
Oklahoma City, Okla.
Omaha, Nebraska-Iowa
Paterson-Clif ton-
Passaic, N. J.
Phi lade 1 phis, Pa.-N..T.
Phoenix, Ariz.
Pittsburgh, P<*.
Portland, Oreg.-Wash.
Providence-Pawtucke t-
Warvlck, R. f.-Mass.
Richmond, Va .
Rochester, N.".
Sacramento, Cfi .
St. Louis, Mo -111.
Salt Lake City, Utah
San Antonio, 'exas
San Bernadlno- Riverside-
Ontario, Ca
San Diego, Ca
San Francisco-
Oakland, Ca
San Jose, Ca .
Seattle-Everett, Wa.
Sprlngfield-Chicopee-
Holyoke, Mass. -Conn.
Syracuse, N.Y.
Tampa-St. Petersburg,
Fla.
Toledo, Ohio-Mich.
Washington, D.C.-Md.-
Va.
Youngs town-Warren,
Ohio
(x) =
idard Deviation (s) =
Value
2.1143
2.2786
1.9357
0.9500
1.2786
1.5929
2.6786
1.0786
2.3357
2.3214
1.5929
2.0357
1.3714
0.7857
1.2000
1.8786
1.8357
1.7500
2.1071
1.1357
1.2071
1.6214
2.1714
1.8571
1.5857
1.7390
.5475
Rating
B
B
B
E
D
C
A
E
A
A
C
B
D
E
D
C
C
C
B
E
D
C
B
C
D
Political
Value
2.8056
2.5833
1.8542
2.4306
1.9097
3.1181
3.5486
3.0347
2.4722
3.6667
3.6181
2.5833
3.3542
1.3403
2.6944
3.1111
2 . 9444
2.9167
3.0347
2.6667
3.6458
1.9514
3.0278
2 . 3403
2.7222
2.6219
0.6466
Rating
B
C
E
D
E
B
A
B
C
A
A
C
A
E
C
B
B
B
B
C
A
E
B
D
C
Environmental
Value
-0
-1
-1
-1
-0
-1
-0
-0
-1
-0
-0
-1
-1
-0
-0
-0
-0
-e
-0
-0
-i
-i
-i
-0
-0
LI
0
.8250
.3063
.0000
.0250
.5917
.8667
.6500
.7667
.1333
.7000
.2000
.5833
.0250
.8333
.4750
.5333
.7000
.5333
.2667
.6167
.1500
.0583
.1833
.8333
.9667
.0342
.3452
Rating
B
D
C
C
A
E
A
B
D
B
A
E
C
B
A
A
B
A
A
A
D
C
D
B
C
Health and Education
Value
1
1
1
0
1
0
2
-0
0
2
2
0
2
0
1
.3750
.7500
.4625
.3000
.6000
.7875
.1375
.1750
.4500
.0000
.1875
.5625
.5625
.2875
.3625
Rating
B
B
B
E
B
D
A
E
D
A
A
D
A
E
B
1.8125 B
2
2
2
0
1
0
0
2
0
1
0
.3750
.7250
.2625
.7000
.8500
.0000
.9375
.1000
.6375
.1252
.7868
A
A
A
D
B
E
C
A
D
Social
Value
O.S852
0.9966
0.1371
0.2234
0.7246
0.3510
1.0273
0.1606
0.1123
0.2196
0.9576
0.1583
0.5728
0.2463
0.6042
0.9020
0.8189
0.7364
1.0144
0.4634
0.6157
0.5526
0.5617
0.6848
0.3634
0.4809
0.2928
Rating
A
A
E
D
B
D
A
E
E
D
A
E
B
D
B
A
A
B
A
C
B
C
C
B
D
1.2710
1.2600
.8779
.5758
.9842
.7966
1.7484
.6664
.8474
1.5015
1.6312
.7513
1.3672
.3653
1.0772
1.4342
1.4548
1.5190
1.6304
.8698
1.2337
.6134
1.1030
1.2298
.8684
.9865
.3688
Outstanding (£ < + s)
Excellent (x + .28s s B < x + s)
Good (x - .28s < C < it + .28s)
Adequate (x-s
-------
TABLE 17
QUALITY OF LIFE INDEXES AND RATINGS IN MEDIUM SMSA'S
Economic
Political
Environmental
Health and Education
Overall
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.
80.
81.
82.
83.
84.
85.
86.
87.
88.
89.
90.
91.
92.
93.
94.
95.
96.
97.
98.
99.
100.
101.
102.
103.
104.
105.
SMSA
Albuquerque, N.M.
Ann Arbor, Mich.
Appleton-Oshkosh, Wla.
Augusta, Ga.-S.C.
Austin, Texas
Bakersfield, Ca .
Baton Rouge, La.
Beaumont-Port An.hur-
Orange, Texas
Binghamton, N.Y.-Pa.
Bridgeport, Conn.
Canton, Ohio
Charleston, S.C.
Charleston, W, Va.
Charlotte, N.C.
Chattanooga, Tenn.-Ga.
Colorado Springs, Colo.
Columbia, S.C.
Columbus, Ga.-Ala.
Corpus Christi, Texas
Davenport-Rock Island-
Moline, Iowa-Ill.
Des Moines, Iowa
Duluth-Superior, Minn.-
Wis.
El Paso, Texas
Erie, Pa.
Eugene , Oregon
Evansville, Ind.-Ky.
Fsyetteville, N.C.
Flint, Mich.
Fort Wayne, Ind.
Fresno, Ca .
Greenville, S.C.
Hamilton-Middleton,
Ohio
Harrisburg, Pa.
Hunt ing ton -Ash land ,
W. Va.-Ky.-Ohio
Huntsville, Ala.
Jackson, Miss.
Johnstown, Pa.
Kalamazoo, Mich.
Knoxville, Tenn.
Lancaster, Pa.
Value
1.8571
2.1429
2.4214
0.9571
1.7857
l-2t43
1.4143
1.7214
1.7071
1.8071
2.1643
0.9643
1.2714
1 . 6643
1.3214
1.5714
1.4286
1.0786
1 . 9000
2.0286
2.2500
1.4000
0.9643
1.6500
2.2000
1.9143
0.6643
2.0000
2.9500
1.0214
1.5643
2.0071
1.5643
1.1643
1.6071
1.3929
1.1786
2.5429
1.7214
1.8357
Rating
B
A
A
E
C
D
D
C
C
B
A
E
D
C
D
C
D
E
B
B
A
D
E
C
A
B
E
B
A
E
C
B
C
E
C
D
E
A
C
B
Value
3.1111
2.5764
3.6528
2.1111
2.3125
3.1667
2.3958
2.0833
3.4375
3.3681
2.7708
1.6458
3.2431
1.9028
2.3889
2.3333
1.5764
1.6319
1.5000
2.6528
3.3333
3.7292
1.6944
2.8681
3 . 5000
3.2500
1 . 6042
3.2917
3.3750
3.0000
1.6944
2.3542
2.4514
2.4931
2 . 1042
1.6944
2.9375
3.5069
2.4236
2.1806
Rating
B
C
A
D
D
B
D
n
A
A
C
E
A
£
D
D
E
E
E
C
A
A
E
B
A
A
E
A
A
B
E
D
D
C
D
E
B
A
D
D
Value
-1.2750
-0.9083
-0.9417
-1.0583
-1.0583
-0.6167
-1.0583
-0.95fl3
-1.0583
-0.8083
-1.1917
-1.2417
-1.3000
-1.3917
-1.0917
-1.1333
-1.4750
-1.2250
-0.3917
-0.6000
-0.9583
-0.5333
-1.0417
-0.8917
-0.5833
-0.9750
-1.0417
-1.0083
-0.9417
-0.2833
-1.1917
-0.8500
-0.8583
-1.5750
-1.2000
-1.0917
-1.2083
-0.8583
-0.7583
-1.0250
Rating
E
C
C
D
D
A
D
C
D
B
D
D
E
E
D
D
E
D
A
A
C
A
C
C
A
C
C
C
C
A
D
B
B
E
D
D
D
B
B
C
Value
2.2000
2.4250
1.8625
0.3250
1.7250
0.9250
1.7250
0.9000
1.9375
1.4625
0.6500
0.0875
0.6500
1.1125
0.1750
1.4750
0.5875
0.1000
0.8000
0.5000
1.7750
1.5375
1.2875
1.0125
2.2875
0.7375
0.3625
1.1250
1 . 3000
1.4500
-0.1875
1.1500
0.9875
0.0750
1.2500
0.8500
0.5125
1.6375
0.9750
0.5875
Rating
A
A
A
E
B
C
B
C
A
B
D
E
D
C
E
B
D
E
D
D
A
B
B
C
A
D
E
C
B
B
E
C
C
E
C
D
D
B
C
D
Value
0.4704
1.0205
1.1075
0.0539
0.7041
0.2502
0,5199
0.4404
0.6848
0.5826
0.3160
-0.1268
0.3726
0.5993
0.0014
0.8953
0.0657
-0.0701
0.4818
0 . 5864
1.3197
1.0333
0.4601
0.5385
1.2617
0.4387
0.0068
0.5172
0.8673
0.6579
0.1535
0.2516
0.4825
0.0780
-0.1253
0.0691
0.3667
0.8011
0.2258
0.1355
Rating
C
A
A
E
B
D
C
C
B
C
D
E
D
B
E
A
E
E
C
C
A
A
C
C
A
C
E
C
A
B
D
D
C
E
E
E
D
B
D
E
Value
1.2727
1.4513
1.6205
.4778
1.0938
.9979
.9993
.8374
1.3417
1.2824
.9419
.2658
.8474
.7774
.5590
1.0283
.4366
.3031
.8580
1.0336
1.5439
1.4333
.6729
1.0355
1.7332
1.0731
.3192
1.1851
1.5101
1.1692
.4066
.9826
.9255
,4471
.7272
.5829
.7574
1.5260
.9175
.7429
Ratli
B
A
A
E
B
C
C
D
B
B
C
E
D
D
E
C
E
E
D
C
A
A
D
C
A
C
E
B
A
B
E
C
C
E
D
E
D
A
C
D
214
-------
TABLE 17 (Concluded)
QUALITY OF LIFE INDEXES AND RATINGS IN MEDIUM SMSA'S
Political
Environmental
Health and Education
SMSA
Lansing, Mich.
Las Vegas, Nev.
Lawrence-Haverhill ,
Mass.-N.H.
Little Rock-North
Little Rock, Ark.
Lorain-Elyria , nhio
[owe 11, Mass
Macon, Ca.
Madison, Wis.
Mobile, Ala.
Montgomery, Ala.
New Haven, Conn.
New London-Grot on-
Norvich, Conn.
Newport News-Hanpton,
Va.
Orlando, Fla.
Oxnard-Ventura, Ca.
Pensacola, Fla.
Peorla, 111.
Raleigh, N.C.
Reading, Pa.
Rockford, 111.
Saginaw, Mich.
Sal tnas-Monterev, Ca.
Santa Barbara, Ca.
Santa Rosa, Ca.
Scranton, Pa.
Shreveport , La .
South Bend, Ind
Spokane, Wash.
Stamford, Conn.
Stockton, Ca.
Tacoma, Wash.
Trenton, N.J.
Tucson, Ariz.
Tulsa, okla
Utica-Rome, N.Y
Vallejo-Napa, C,l .
Waterbury, Conn
West Palm Beach Fla.
Wichita, Kansas
Wilkes-Barre-
Hazelton, Pa.
Wilmington, Del . -
N.J.-Md.
Worcester, Mass.
fork, pa.
K) =
rd Deviation (s} =
Value
2.0929
1.6786
1.8000
1.4000
1.9643
1.4571
0.9357
1.7857
1.1143
0.7500
2.0429
1.3357
1.3214
1.4500
1.3929
1.1857
2.4071
1.8214
1.6714
2.2071
2.4071
1.1857
1.6786
1.6000
1.4786
1.5071
2.7000
1.5214
2.4714
1.6071
1.1500
1 . 3000
1.2000
2.4429
1.2786
1.5786
2.1429
2.4786
2.1714
1.4500
1.6786
1.6643
1.9643
1.6691
0.4695
Rating
B
C
C
D
B
D
E
C
E
E
B
D
D
D
D
E
A
B
C
A
A
E
C
C
D
D
A
D
A
C
E
D
D
A
D
C
A
A
A
D
C
C
B
Value
3.3194
2.3403
3.1319
1.7917
2.4792
2.9653
1.5417
3.5069
1.7708
1.9722
3.3056
2.8264
2.0347
2.4722
2.8611
2 . 0000
2.6528
2.4306
2.3958
2.5972
2.7222
2.0694
3.4444
3.3194
3.0625
1.9514
3.3264
3.0694
2.9097
2.8542
2.2014
2 . 7500
2.3264
2.6736
3.2222
2.6111
3.3889
2 . 3542
3.0764
2.7431
2.8472
3.0000
2.0903
2.6236
0.5970
Rating
A
D
B
E
C
B
E
A
E
E
A
B
D
C
B
E
C
D
D
C
C
D
A
A
B
E
A
B
B
B
D
C
D
C
A
C
A
D
B
C
B
B
D
Value
-0.9417
-0.3417
-0.6833
-1.1917
-1.1750
-0.8833
-1.2250
-0.9083
-1.4917
-1.2500
-0.8750
-0.8750
-0.6417
-1.1083
-0.6000
-1.2250
-1.0750
-1.1750
-1.1500
-0.7000
-0.9250
-0.3000
-0.5667
-0.8833
-1.3083
-1.4083
-1.0417
-1.0167
-0.7083
-0.8750
-0.0667
-0.6583
-0.8833
-1.6250
-0.9417
-0.8500
-0.7833
-1.3583
-1.0250
-1.2333
-0.7917
-0.9000
-1.1833
-0.9700
0.2961
Rating
C
A
B
D
D
B
D
C
E
D
B
B
A
D
A
D
D
D
D
B
C
A
A
B
E
E
C
C
B
B
A
A
B
E
C
B
B
E
C
D
B
C
D
Value
2.4250
0.8250
1.3750
0.7750
0.7000
1.3750
0.0625
2.9250
0.0250
-0.0250
1.4625
0.8250
0.5625
0.5375
1.7125
0.5500
0.7500
1.4375
0.2750
0.8125
0.7750
2.0750
2.3750
1.4000
0.3250
0.8625
1.1375
1.5875
2.3500
1.2625
0 . 8000
0.9375
2.1750
1.2750
1.2625
1.3750
0.7125
0.6875
1.8250
0.2125
1.1000
0.9125
0.3125
1.0799
0.6777
Rating
A
D
B
D
D
B
E
A
E
E
B
D
D
D
B
D
D
B
E
D
D
A
A
B
E
D
C
B
A
C
D
C
A
B
C
B
D
D
A
E
C
C
E
Value
0.7408
0.8404
0.6545
0.3733
0.3523
0.5119
0.0200
1.2014
-0.2661
-0.1114
0.6692
0.5058
0.3679
0.3552
0.4437
0.0217
0.5174
0.3074
0.2705
0.5126
0.3535
0.6651
0.9701
0.7239
0.5358
0.1250
0.6098
1.1078
0.8212
0.6136
0.9543
0.3168
0.5731
0.5416
0.4485
0.6496
0.4734
0.7189
1.1741
0.1482
0.3135
0.9578
0.1015
0.4901
0.1111
Rating
B
B
B
D
D
C
E
A
E
E
B
C
D
D
C
E
C
D
D
C
D
B
A
B
C
E
B
A
B
B
A
D
C
C
C
S
C
B
A
D
D
A
E
Value
1.5273
1.0685
1.2556
.6297
.8642
1.0852
.2670
1.7021
.2305
.2672
1.3210
.9236
.7290
.7413
1.1620
.5065
1.0505
.9644
.6925
1.0859
1.0666
1.1390
1.5803
1.2320
.8187
.6075
1.3464
1.2539
1.5688
1.0925
1.0078
.9292
1.0782
1.0616
1.0540
1.0729
1.1869
.9762
1 .4444
.6641
1.0295
1.1269
.6571
0.9781
0.3649
Rating
A
C
B
0
D
B
E
A
E
E
B
C
D
D
B
E
C
C
D
B
C
B
A
B
D
E
A
B
A
B
C
C
C
C
C
C
B
C
A
D
C
B
D
[standing (» x -t s)
:ellent (x + .2Ss s B < X + s)
3d (x - .28s .- C f x + .28s)
;quate (x-s^D
-------
TABLE 18
QUALITY OF LIFE INDEXES AND RATINGS IN SMALL SMSAVS
Economic
149.
150.
151.
152.
153.
154.
155.
156.
157.
158.
159.
160.
161.
162.
163.
164.
165.
166.
167.
168.
169.
170.
171.
172.
173.
174.
175.
176.
177.
178.
179.
180.
181.
182.
183.
184.
185.
186.
187.
188.
189.
190.
191.
192.
193.
194.
195.
196.
197.
198.
SMSA
Abilene, Texas
Albany, Ga.
Altoona, Pa.
Amarillo, Texas
Anderson, Ind.
Ashevllle, N. C.
Atlantic City, N. J.
Bay City, Mich.
Billings, Mont.
Biloxi-Gulfport, Miss.
Bloomington-
Normal, 111.
Boise City, Idaho
Bristol, Conn.
Brockton, Mass,
Browns vi lie- Harlingen-
Bryan-College
Station, Texas
Cedar Rapids, Iowa
Champaign-Urbana, 111.
Columbia, Mo.
Danbury, Conn.
Decatur, 111.
Dubuque, Iowa
Durham, N. C.
Fall River, Mass.-
R. I.
Fargo-Moorhead ,
N. Dsk.-Mlnn.
Fitchburg-Leoninster,
Mass.
Fort Smith, Ark.-
Okla.
Gadsden, Ala.
Gainesville, Fla.
Galveston-Texas City,
Texas
Great Falls, Mont.
Green Bay, Wia.
Jackson, Mich.
Kenosha, Wis.
La Crosse, Wis.
Lafayette, La.
Lafayette-West
Lafayette, Ind.
Lake Charles, La.
Laredo, Texas
Lawton, Okla.
Lewiston-Auburn ,
Maine
Lexington, Ky.
Lima, Ohio
Lincoln, Neb.
Lubbock, Texas
Lynchburg, Va.
Manchester, N. H.
Mansfield, Ohio
McAIIen-Pharr-
Edinburg, Texas
Meriden, Conn.
Value
1.9214
0.4643
1.2143
2.7500
2.3429
1.9000
0.7643
2.3071
1.8429
0.5857
1.9000
2.3857
2.2571
1.1786
0 2714
1.6643
2.3214
1.4786
1.5214
2.1429
2.5929
1.9857
1.8786
1.1214
1.7929
1.6929
0.9929
0.8429
0.9214
2.1357
0.8643
2.3429
2.2143
1.9643
2.1000
0.8500
2.1429
1.1500
0.0571
0.6000
0.9571
1.9357
1.7071
2.7571
2.0214
2.0429
2.0571
2.0214
0.5071
1.9429
Rating
B
E
D
A
B
C
E
B
C
E
C
B
B
D
E
C
B
D
D
B
A
B
C
D
C
C
E
E
E
B
E
B
B
B
B
E
B
D
E
E
E
B
C
A
B
B
B
B
E
B
Political
Value
•1.8929
1.4008
2.5476
2.2857
3.1905
2.4683
3.3214
3.6151
3.3095
1.9087
2.9246
3.2817
3.1349
2.8333
1 *> ')')')
l > LLLL
2.0714
3.1508
2.0873
2.5873
3.6190
2.6151
3.3651
2.0317
2.8016
3.3651
3.3333
1.5159
2.0873
1.7619
2.1706
2.4643
3.3849
2.8373
2.9643
3.8016
1.6190
3.0675
1.7976
1.3690
1.3730
2.8810
2.0516
2.7579
2.8016
2.2857
2.1548
3.3532
2.6071
1.3413
3.3532
Rating
E
E
C
D
B
C
A
A
A
E
B
A
B
B
E
D
B
D
C
A
C
A
D
C
A
A
E
D
E
D
C
A
B
B
A
E
B
E
E
E
B
D
C
C
D
D
A
C
E
A
Environmental
Value
-0.0417
0.1250
-0.0833
0.0833
-0.0417
0.4583
-0.0417
-0.3333
-0.2917
-0.2917
0.5833
-0.2917
0.9167
0.0000
0, 4583
-0.2083
0.0000
-0.2500
-0.2500
0.4167
0.5833
0.3750
0.0833
-0.0833
0.0000
1.1250
0.6250
0.5833
-0.2083
0.1250
0.4583
0.4583
1.3333
-0.0417
0.0000
-0.3750
0.0000
-0.2917
-0.3333
-0.6667
-0.3333
0.0833
-0.3750
0.3750
-0.3333
0.1667
0.7083
-0.0417
0.0417
1.0417
Rating
D
C
D
C
D
B
D
E
E
E
A
E
A
D
B
D
D
E
E
B
A
B
C
D
D
A
A
A
D
C
B
B
A
D
D
E
D
E
E
E
E
C
E
B
E
C
A
D
D
A
Health and
Value
0.9167
0.7292
0.2917
1.7292
0.7292
0.8125
-0.0417
1.2083
2.0000
1.3125
1.7083
1.2500
1.2917
0.8333
0.6667
2.3542
1.3333
2.0000
2.7917
1.3333
0.7292
0.7917
1.5417
0.1458
2.2708
0.5833
-0.4167
-0.2500
2.6458
1.3333
1.6667
1.4583
0.8125
0.7917
2.1667
1.5833
2.2292
0.7708
0.6458
0.9792
-0.3750
1.4167
0.3750
2.1667
1.4583
0.0625
0.4583
0.4375
0.5208
0.5167
Education
Rating
C
D
E
B
D
D
E
C
A
B
B
C
C
D
D
A
B
A
A
B
D
D
B
E
A
D
E
E
A
B
B
B
D
D
A
B
A
D
D
C
E
B
D
A
B
E
D
D
D
D
Social
Value
0.5198
0.1927
0.4158
0.7387
0.2506
0.2266
0.0448
0.3497
1.0761
0.2225
0.8250
0.7689
0.7228
0.4370
0. 1202
0.2265
0.5359
0.5211
0.7782
0.7511
0.6225
0.7862
0.5900
0.1497
1.0028
0.6858
-0.2266
0.0363
0.5839
0.3493
0.7300
1.1032
0.4329
0.3637
1.4668
0.2263
0.6378
0.3063
0.2451
0.4396
0.8716
0.3373
0.2131
1.1356
0.5378
-0.0461
0.9797
0.3511
0.0489
0.4795
Rating
C
D
C
B
D
D
E
D
A
D
B
B
B
C
E
D
C
C
B
B
B
B
C
E
A
B
E
E
C
D
B
A
C
D
A
D
B
D
D
C
A
D
D
A
C
E
A
D
E
C
Overall
Value
1.0418
.5824
.8772
1.5174
1.2943
1.1731
.8094
1.4294
1.5874
.7475
1.5882
1.4789
1.6646
1.0564
. 5478
1.2216
1.4683
1.1674
1.4857
1.6526
1.4286
1.4607
1.2251
.6830
1.6863
1.4841
.4981
.6600
1.1409
1.2228
1.23i>7
1.7495
1.5261
1.2085
1.9070
.7807
1.6155
.7466
.3967
.5450
.8003
1.1649
.9356
1.8472
1.1940
.8762
1.5113
1.0751
.4920
1.4468
Rattnj
D
E
D
B
C
C
E
B
B
E
B
B
A
D
E
C
B
C
B
A
B
B
C
E
A
B
E
E
C
r
c
A
E
C
A
E
A
E
E
E
E
C
D
A
C
D
B
D
E
B
216
-------
TABLE 18 (Concluded)
QUALITY OF LIFE INDEXES AND RATINGS IN SMALL SMSA'S
Economic
199.
200.
201.
202.
203.
204.
205.
206.
207.
208
209.
210.
211.
212.
213.
215.
216.
217.
218.
219.
220.
221.
222.
223.
224.
225.
226.
227.
228.
229.
230.
231.
232.
233.
234.
235.
236.
237.
238.
239.
240.
241.
242.
243.
lean
SMS*
Midland, Texas
Modtito, Ollf.
Monroe, La,
MuncU, Ind.
Heights, Mich.
Nashua. N. H.
New Bedford,
Mass.
New Britain, Conn.
Norvalk, Conn.
rt,;Jen, Utah
Owensboro, Ky.
Petersburg-Colonial
Heights, Va.
Pine Bluff, Ark.
Pittsfteld, Ma»s.
Provo-Orem, Utah
Pueblo, Colo.
Racine, Wls.
Reno, Nevada
Roanoke , Va .
Rochester, Minn.
St . Joseph, Mo.
Salem, Oregon
San Angelo, Texas
Savannah, Ca.
Sherman- Den i son,
Texas
Sioux City, lowa-
Nebr.
Sioux Falls, S. Dak.
Springfield, 111.
Springfield, Mo.
Springfield, Ohio
Steubenville-Weirton,
Ohlo-W. Va.
Tallahassee, Fla.
Terre Haute, Ind.
Texarkana, Texas-
Ark.
Topeka, Kansas
Tuscaloosa, Ala.
Tyler, Texas
VlneUnd-Millville-
Brldgeton, N. J.
Waco, Texas
Waterloo, Iowa
Wheeling,
W. Va.-Ohio
Wichita Falls, Texas
Wilmington, N.C.
(*) .
Standard Deviation (s) "
Value
2. 7U3
1.7929
1.1171
2.3286
1.7857
1.6857
1 . 0500
1.7786
2.6214
1.6143
1 . 7000
1.0571
0.6929
1.8429
1 . 7786
0.7071
1.6429
2.4214
2.5071
2.5143
1.5571
2.2500
2.2786
2.4214
0.9214
2.2714
1 . 7000
1.8857
2.4643
2.4857
2.0143
2.0143
1.5286
2.2000
1.9429
2.6857
0.7286
2.7643
0.8929
1.9786
1.9357
1.6786
2.3071
0.9571
1.7372
0.6491
Bating
A
C
D
B
C
C
I
C
A
C
C
E
E
C
C
E
C
A
A
A
C
B
B
A
E
B
C
C
A
A
B
B
D
B
B
A
E
A
E
B
B
C
8
C
Political
Value
2.9484
2.8690
1.8333
3.1706
3.4127
3.0833
2.9563
2.6190
3.0476
2 . 2143
2.4960
2.2302
2.3333
1.3214
3.6627
3 .0079
2.5913
3.3770
3.0278
2.6111
2.4365
3.0675
2.6865
2.6905
2.1865
1.6429
2.4643
3.0913
3.3889
3.0040
2.9444
2.4643
3.0873
2.7302
3.6111
2.1825
3.2579
1.3214
2.2540
2.4881
2.1627
3.0000
3.3571
2.0357
2.2500
2.6293
0.6464
Hating
B
B
E
B
A
B
B
C
B
D
A
D
D
E
A
B
C
A
B
C
D
B
C
C
D
E
C
B
A
B
B
C
B
C
A
D
B
E
D
C
D
B
A
D
D
Environmental
Value
-0.4583
0.3333
-0.16(7
0.4167
0.5000
-0.2083
-0.0833
-0.0833
0.0000
-0.0833
0.4167
-0.0833
0.0833
0.2917
0.9167
-0. 0417
0.5000
0.0417
0.0833
0.2083
0.1250
0.7500
0.6667
0.8750
1.1667
0.2083
0.1250
0.5000
-0.1250
0.2083
0.1250
-0.2083
0.1250
0.4583
0.0417
-0.4167
-0.0417
0.1667
0.8750
0.0833
0.3750
0.5833
-0.2917
-0.0417
0.2083
0.1592
0.4026
Rating
t
t
S
B
„
D
D
D
D
D
B
D
C
B
A
D
D
C
C
C
A
A
A
A
C
C
B
D
C
C
D
C
B
D
E
D
C
A
C
B
A
E
D
C
Health and Education Social
Value
1.9583
0,8750
0.7500
1.1042
1.0417
1.1458
0.0417
0.8542
2.0417
1 . 0208
2.4167
1.5625
0.6875
0.0208
0.7708
0. 7917
2.2917
1.2083
1.0417
1.7500
1.0625
2.6875
0.3958
1 . 7083
1.2292
0.3125
1.3958
0.8333
2.2917
0.9167
1.2083
0.8542
0.2292
2.4583
0.7083
0.2917
2.0625
0.8333
0.9167
0.3125
0.4375
1.5417
0.3750
0.6250
0.1250
1.0932
0.7368
Ratlin
A
D
D
C
C
C
E
0
A
C
A
B
D
E
D
D
A
C
C
B
C
A
D
B
C
E
B
D
A
C
C
D
E
A
D
E
A
D
C
E
D
B
D
D
E
Value
0.6024
0.1461
0.2938
0.2443
0.4360
0.5257
0.0599
0.6735
0.8007
0. 7754
0.6713
0.2863
0.1233
-0.1229
0.8211
0.6884
0.8749
0.5784
0.3585
1.0046
0.4196
1.2354
0.8899
0.4244
0.8204
0.1233
0.5271
0.6545
1.0083
0.7625
0.7363
0.1460
0.0194
0.5683
0.3948
-0.2097
1.1026
-0.0177
0.4105
0.2427
0.3823
0.8065
0.1664
0.6269
-0.1506
0.4957
0.3451
Rating
B
E
D
0
C
C
E
B
B
B
B
D
E
E
8
B
A
C
D
A
C
A
A
C
B
E
C
B
A
B
B
E
E
C
D
E
A
E
C
D
D
B
D
B
E
Over
Value
1.5530
1.2033
.7735
1.4S29
1.4352
1.2464
.8049
1.1684
1.7023
1 . 2597
1.7230
1.1391
.8569
.4408
1.6028
1. 2450
1.3930
1.3697
1.3865
1.6162
1.3116
1.8595
1.3778
1.5954
1.5648
.6417
1.3567
1.3558
1.6899
1.4712
1.4999
1.0541
1.0950
1.5487
1.3912
.7581
1.8134
.6065
1.4441
.80J9
1.0672
1.5734
1.0571
1.1106
.6780
1.2214
.3778
Si!
Ratlni
B
C
I
B
B
C
C
C
A
C
A
C
D
E
A
C
B
B
B
A
C
A
B
B
B
E
B
B
A
B
B
D
D
B
B
E
A
E
B
E
D
B
D
D
E
A - Outstanding (2 x + s)
B - Excellent (x + .28s £ B < x + s)
: - Good (5c - .28s
-------
Since both methods of the standardized and the adjusted standardized
scores produced highly consistent rankings with the rank-order correlation
coefficients higher than 0.95 in all but environmental quality of life
components, only the adjusted standardized results are presented.
It should be noted that the summary tables include an overall quality
of life rating. This composite index is simply the weighted average of
the five individual components. The overall indexes are presented with
a certain degree of hesitancy since any effort to describe the quality
of life by a single measure may not be particularly informative and may,
in fact, be misleading. Economists may employ the GNP to measure the
flow of goods and services produced in any year; however, the quality
of life is a stock concept which may only be approximated by a set of
component indicators. It is our belief that only by looking below
the surface—by analyzing the individual components and subcomponents--
is it possible to determine why a metropolitan area performed the way
it did and what the particular areas of strength and weakness are.
The most important findings in this study and their implications are
broadly delineated as follows:
1. Although it is normally expected that the levels of objectively
measured quality of life vary from region to region and from component
to component, it is very interesting to note that only five of the
243 SMSA's—three in the large group and two in the medium sized group--
showed exactly the same ratings for each of the five quality of life
components. In other words, this finding implies that in this country
there is neither a perfect region offering the best of all quality of
life nor a worst region suffering substandard quality of life in all
components. Some SMSA's rated high in one or more components but
not in others; the reverse is also true. Two important implications
are deduced from this observation. First, for policy decision makers,
it indicates that there is (are) always an area (.or areas) requiring special
attention and extra effort in order to balance the overall satisfaction
in our quality of life. This study identifies the relative weaknesses
for each SMSA in terms of quality of life components or factors.
Secondly, for social indicator students, it points out the difficulty
of constructing a single index to reflect the overall quality of life
or the social well-being for a specific region at a specific point in
time. Quality of life is a notion for multidimensional concepts.
Thus, at the present time it is not only theoretically controversial
to consider a sole indicator for the overall social welfare, but it
is also empirically difficult to single out an index for the multi-
dimensional quality of life measurements, due to the lack of concensus
in weighting among the quality of life components.
218
-------
2. This study covering all metropolitan areas found that although the
Pacific, the East North Central, the Mountain, and the New England
regions had relatively more SMSA's with outstanding and excellent
ratings than the other regions, they also had substandard areas, though
relatively fewer in number. In contrast, the southern states did show
relatively larger numbers of low rated SMSA's, but they also include
some SMSA's with quality of life measures beyond the "adequate" or
"good" category, e.g., West South Central and South Atlantic regions
showed up fairly strong in the economic component. In other words, the
hypothesis is inconclusive with respect to this test of regional
differential, and much less significant in this metropolitan study than
in an earlier state study. The implication of this is that, for urban
policy to be efficient and effective, each SMSA must be examined
independently and its priorities set individually. The state data
are usually insufficient, if not misleading for providing basic policy
guidance for SMSA action programs.
3. It has been frequently asserted that money cannot always buy
happiness.— In a like manner, many previous studies have argued that
quality of life is not necessarily a direct function of income and
material wealth, at least beyond a certain level of subsistence. In
the quality of life study for all states, for instance, we found that
some states ranked fairly high in terms of quality of life ratings,
but had relatively low personal income per capita.—' The findings of
this metropolitan study tend to validate that conclusion in that
SMSA's which had outstanding ratings in the economic component did not
simultaneously have outstanding ratings in social, political, environ-
mental, and health and education components. Indeed, there are just as
many, if not more,SMSA's with relatively high ratings in the other
quality of life components but relatively low ratings in the economic
components as the reversed combination. The association between
economic component ratings and other quality of life component ratings
is also weaker among the large SMSA's than that among the medium and
the small SMSA's. The implication is that policies focusing on
economic growth alone do not concomitantly guarantee the betterment of
quality of life concerns', especially in the large SMSA's.
6/ For instance, see R. A. Easterlin, "Does Money Buy Happiness?"
The Public Interest, .30 (Winter 1973).
Tj See Ben-chieh Liu, "Quality of Life: Concept, Measure and Results,"
The American Journal of Economics and Sociology, Vol. 34, No. 1
(January 1975), pp. 1-13.
219
-------
4. Despite the generally weak relationship between the economic com-
ponent and other quality of life components, the trade-off or inverse
relationship, between economic development and environmental quality
was highlighted in the large metropolitan areas, especially in the East
North Central Region. This inverse relationship was not as evident in
the medium and small SMSA's. To avoid the adverse impacts of economic
growth on environment, or to alleviate the degree to which the trade-
offs may occur, appropriate environmental protection policies and
careful planning for the future seem to be timely for the medium and
small SMSA's. The large SMSA's in the Pacific, the Mountain and the
West South Central regions showed significantly better environmental
quality than those in other regions.
5. The conventional statement that political quality and economic
attributes are bound hand in hand is not strongly supported by this
study. There is a general, positive correlation between the two on
a geographical basis if the country is divided into two parts,
north and south. Politically, the SMSA's in the North rated rela-
tively more favorable than those in the South. Proportionately more
of the SMSA's surrounding the Great Lakes, in the Middle Atlantic,
East North Central, and many in New England and the Pacific region are
found to have outstanding political quality of life. Even though
there is a general dividing line, the extent of quality variation
among SMSA's, as measured by this study, is the smallest among the
five quality of life components for all three SMSA size groups. The
smallest quality variation implies that people in this democratic
country enjoy on the whole, a relatively similar quality of public
goods and services, regardless of their regional location.
6. Most of the large and medium SMSA's in the Pacific region and
many of the medium and small SMSA's in the West and East North Central
regions showed either excellent or outstanding quality of life in
health and education. Although there are only a dozen SMSA's in the
Mountain states, more than one-half of them were ranked outstanding in
the health and education component. The South Atlantic, Middle Atlantic
and East South Central regions, on the other hand, lagged significantly
in health and education quality as compared to other regions. This
regional phenomenon is much more evident in the medium SMSA's than in
the large and the small SMSA's.
220
-------
This regional inequality pattern in terms of health and educational
quality distribution deserves further attention. The coefficient of
variation among the health and education indexes was more than twice
as large as those among the economic, political and environmental
indexes. The health and educational quality coefficient of variation
among the large SMSA's (70.0 percent) is the highest among the three
groups of cities. The coefficients for the medium and small sized
groups are, respectively, 62.0 percent and 68.0 percent.
The high coefficient of variation implies that there exists an
appreciable quality variation among the SMSA's as far as the health
and education factors are concerned. This high variation in quality,
compounded with the pattern of geographic concentration, suggests
that there are serious problems of human resource development in cer-
tain sections of this country. Investments in human capital which
bring about greater mobility, better health, and higher technological
learning capability among individuals are therefore necessary if a
national objective is to equalize the health and educational differen-
tials both geographically and among individuals.
7. The regional inequality pattern observed for the health and education
component is also prevalent in the social component. Figures 5 and
10 show an intensive concentration of high ratings in social quality
of life in the West Coast and in the East and West North Central
regions. For the small SMSA's, most of "A" and "B" ratings were dis-
played in the West North Central and the Mountain regions, except for
a couple of SMSA's in New England which also demonstrated excellent
or outstanding quality of life in social considerations. The concen-
tration of substandard medium SMSA's in the South Atlantic and the
East South Central regions is most striking, as shown in Figure 10.
In addition to the regional concentration phenomenon, the quality of
life indexes in the social component also exhibited as significant
variations as did those in the health and education component. The
highest coefficient of variation of the indexes was found for the
medium size SMSA's (71.0 percent); the small SMSA's were next with
the coefficient being 70,0 percent, and for the large SMSA's the co-
efficient was relatively smaller (61.0 percent). As discussed
earlier, the significant variations in quality and the unequal
geographical distribution inevitably suggest the need for appropriate
policies, both national and regional, to cope with those factors ad-
versely affecting our social quality of life in the lagging areas.
221
-------
In short, the findings in this study tend to indicate that the major
disparities in quality of life are neither in the economic nor in
the political component; rather, they are in social, health and edu-
cation and to a lesser degree, in environmental concerns. The
geographic differentials and apparent concentrations of adverse quality
of life conditions present special problems which warrant targeted
policies and actions.
8. As noted earlier, the overall QOL indexes which were simply taken
as the average of the component indexes are presented only for the
satisfaction of general curiosity since it is well understood that the
overall QOL production perceived by any individual is not necessarily
a simple, linear-additive from the five components. Nevertheless,
scientific knowledge so far is still unable to derive a social welfare
or utility function on which a general concensus with respect to the
variable definition, measurement and weighting scheme can be deduced.
As a result, the overall indexes so developed serve as no more than a
rough QOL comparison over regions in this country, and hopefully it
may stimulate more profound, useful research in the area of social
welfare measurements.
Given those words of caution, it may be of interest to note that most
of the large outstanding SMSA's are in the North and the Pacific--
Denver, Grand Rapids, Hartford, Milwaukee, Minneapolis/St. Paul,
Portland, Rochester, Sacramento, Salt Lake City, San Diego, San Francisco/
Oakland, San Jose, and Seattle/Everett, while most of the large sub-
standard SMSA's are in the South--Birmingham, Greensboro/Winston-
Salem/High Point, Jacksonville, Jersey City, Memphis, New Orleans,
Norfolk/Portsmouth, Philadelphia, San Antonio, and Tampa/St. Petersburg.
For the medium SMSA's, the concentration pattern differs slightly from
the large SMSA's with most of the outstanding SMSA's in the East North
Central region--Ann Arbor (Michigan), Appleton/Oshkosh (Wisconsin),
Des Moines (Iowa), Duluth/Superior, Eugene (Oregon), Fort Wayne (Indiana),
Kalamazoo (Michigan),Lansing (Michigan), Madison (Wisconsin), Santa
Barbara (California), South Bend (Indiana), Stamford (Connecticut),
and Wichita (Kansas), and most of the substandard ones fall again in
the South--Augusta (Georgia/South Carolina), Charleston (South Carolina),
Chattanooga (Tennessee/Georgia), Columbia (South Carolina), Columbus
(Georgia/Alabama), Fayetteville (North Carolina), Greenville (South
Carolina), Huntington/Ashland (West Virginia/Kentucky/Ohio), Jackson
(Mississippi), Macon (Georgia), Mobile (Alabama), Montogomery (Alabama),
Pensacola (Florida), and Shreveport (Louisiana).
222
-------
As far as the small SMSA's are concerned, the geographic distribution
of the ratings tend to be less concentrated relative to the medium and
the large SMSA's. However, there are more outstanding SMSA's in the
East North Central than other regions and the substandard SMSA's are
scattered in the country. The 14 outstanding SMSA's are Bristol
(Connecticut), Danbury (Connecticut), Fargo/Moorhead (North Dakota/
Minnesota), Green Bay (Wisconsin), La Crosse (Wisconsin), Lafayette/
West Lafayette (Indiana), Lincoln (Nebraska), Norwalk (Connecticut),
Ogden (Utah), Pittsfield (Massachusetts), Reno (Nevada), Rochester
(Minnesota), Sioux Falls (South Dakota), and Topeka (Kansas), and the
13 substandard SMSA's are Albany (Georgia), Atlantic City (New Jersey),
Biloxi/Gulfport (Mississippi), Brownsville/Harlingen/San Benito
(Texas), Fall River (Massachusetts/Rhode Island), Fort Smith
(Arkansas/Oklahoma), Gadsden (Alabama), Lafayette (Louisiana), Lake
Charles (Louisiana), Laredo (Texas), Lawton (Oklahoma), Lewiston/
Auburn (Maine), and McAllen/Pharr/Edinburg (Texas).
Figures 16, 17, and 18 show the geographical distributions of various
ratings for the three groups of SMSA's.
This study represents a step forward in the social welfare arena be-
cause it theoretically developed a conceptual model for coping with
the arguments in quality of life determination, and empirically employed
the model to systematically quantify the varying elements of urban
quality of life in the U.S. It also represents a monumental statis-
tical task in collecting, organizing, analyzing and presenting the
latest quality of life factors for all of the nation's metropolitan
areas. The comprehensive data presented in the Appendix should be
very useful to researchers and students interested in a variety of
cross-metropolitan studies.
It is our hope that by describing the apparent weaknesses and strengths
among the metropolitan areas, the findings will stimulate and aid
decision makers at all levels in their efforts to improve the overall
quality of life for all people in this country.
223
-------
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226
-------
There is certainly no guarantee at the present early stage in this
type of social indicator research that decision makers, public or
private, will pay much attention to this kind of information. As
Professor Campbell commented about our earlier state study, "The
kinds of data considered in this monograph do not tell us directly
how society's problems are to be solved, but they may serve a useful
purpose in showing where the problems exist."—'
Other limitations of this study hinge upon the model development and
methodology. Undoubtedly, the model can be further refined, and the
quality of life components can be modified and quantified in finer
detail. For instance, actual levels of noise pollution and solid
waste generation should be used rather than employing approximate
indicators in the environmental component. The weakest point of this
study, needless to say, is its failure to account for the psychological
aspects of the individual regarding his perceptions of quality of life.
Attitudinal surveys on a variety of aspects of quality of life eval-
uation for the metropolitan areas should strengthen the reliability
and enrich the substance of this type of study.
The indexes developed in this study are of use only when the SMSA's in
the same size group are compared; intergroup comparisons among SMSA's
with respect to their absolute index values are precluded and inferences
can only be made on a relative basis. In order to be able to make
intergroup comparisons between large and small, large and medium, and
medium and small SMSA's, a similar study based on the U.S. means should
probably be the next task. In order to complete the series of
quality of life study for the U.S., another similar study for the
rural counties is highly recommended.
The model used in this study was confined in its process of develop-
ment to the requirements that it can be employed universally, and the
study can be updated periodically. In other words, all factors
selected in the model are expected to have consistent empirical data
available in the future so that the quality of life status among
metropolitan areas can be studied intertemporally and some comparative
static analyses can be performed. As soon as new statistical data
become available, the study should be repeated to shed light on changes
in quality of life among regions and to evaluate the impacts of
various policies on the level of quality of life over periods of time.
j§/ See A. Campbell, "Measuring the Quality of Life," Michigan Business
Review, 261 (January 1974), pp. 8-10.
227
-------
Since there are definite regional concentration patterns and in-
equalities in the quality of life, a more thorough investigation of
input factors in the average or substandard regions should reveal the
cause-effect relationships and suggest policy alternatives and
feasible remedies.
Within a complicated society such as we have in the United States,
the multidimensional quality of life indicators approach seems to
be the desirable approach. As demonstrated in this study, the direct
social, economic, political, and environmental impacts as well as
the cross-impacts from various quality of life factors are taken into
account. This multidimensional analysis tends to be the fundamental
background for contemplating, evaluating, and creating relatively
large investment projects or making critical policy decisions.
Specifically, at any stage of operation, it is the net change in the
quality of life indicators which should be borne in mind, rather
than the economic benefits and costs or other similar considerations
alone. The externalities or social welfare elements cannot be accurately
measured by the free market system or the price mechanism but are
probably largely reflected in the social accounts through interaction
of the social indicators.
Precisely what quality of life is, no one person can interpret for
another; but the one who lives only for himself definitely could
not enjoy the best. The best quality of life seems to grow out of
harmonious relationships with others, based on attitudes of goodwill,
tolerance, understanding and love. The joy of living may temporarily
rest on present or past glory, but it is the immersion in planning for
the future—the living ahead of one's time--which ensures permanently
the flourising of the joy of life. In a commonwealth society,
happiness does not come from doing what we like to do, but from liking
what we have to. do!
228
-------
APPENDIX
GENERAL INFORMATION
This appendix contains the data from which the five component ratings
were made. Most of the statistics used in this study are combinations
of two or more sets of data and are thus not readily available else-
where. The original raw data, however, were based on a number of govern-
ment documents. In addition, a Midwest Research Institute questionnaire
was sent to each SMSA to gather certain cultural and sports information
not found in published documents. A copy of this questionnaire is
included at the end of the Appendix.
Tables showing all the factors used in the study constitute the major
portion of the Appendix. Preceding the tables for each of the three
sizes of SMSA's is a list of the three letter codes used for the
SMSA's (e.g., AKR is the abbrevation for Akron, Ohio).
Collection of data for the SMSA's was limited in several instances,
particularly for the smaller sized SMSA's and the SMSA's of the New
England states. Since the SMSA's in New England are composed of towns
rather than counties, whenever statistics were based upon county data
the Standard Economic Areas (SEA's), which are composed of counties,
were used if possible. Data for the smaller sized SMSA's (SMSA's with
population of less than 200,000) were also limited, so certain factors
were either eliminated,or similar, but not identical, information
was used instead. Finally, estimations had to be made based on either
state or neighboring SMSA data if no other data could be found. Any
estimated data are marked in the tables by a black dot behind the figure.
Five charts show the data sources for each factor of the five compo-
nents. Each factor is listed by its corresponding code in the variable
charts in the text (e.g., the economic factor "personal income per
capita" is listed as IA). In addition to the source, the year to which
the data apply is also shown. To avoid numerous repetitions how-
ever, the-source for the population figures used in calculating all
"per capita" factors was omitted. The County and City Data Book, 1972,
provides this information in Item 3. The four most frequently used
sources for this study are publications of the Bureau of the Census:
229
-------
1. County and City Data Book, 1972, hereafter referred to as C&C.
2. Census of Population, 1970. either referred to as COP or COP, US
depending upon whether the state parts (COP) or the U.S. Summary
(COP, US) was used.
3. Census of Government, 1967 (COG).
4. Statistical Abstract of the United States (SA).
230
-------
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TABLE A-l
BASIC STATISTICS OF ECONOMIC COMPONENT (L)
us
1 AIVW
<• ALM
J ALL
« ANA
b ATL
f> HAL
7 HIM
H HOS
10 CHI
11 TIN
i
-------
TABLE A-l (Concluded)
Value Added/
Worker In
Manufacturing
(in $1,000)
ItCl
13.50
13. 80
13.70
10.60
15.60
13.70
13.60
13.30
13.10
15.10
13.90
16.30
U.Ofi
14.00
12.2(1
15. HI
15.111
14.5(1
12.4CI
15.30
17.20
14.40
14.10
12.9(1
12.9CI
20.60
13.60
14.30
14.60
16.00
14.10
18.90
13.90
1 0 . 1 (>
13.8(i
13.8Ci
11.9(i
IS.Sd
12.0(i
lb.40
12.lt
11.01
16. 5C
13. 1C
13. 9(
13. OC
12. (K
13. 9C
11. 1C
15.10
20.00
21. SC
14. 1C
17. 5C
10.50
14.60
12. 7C
15. 9C
15. 8C
12.51
12.90
13. 7C
11. 90
15.50
13.10
15.20
V.I vie of
Construction/
(In $1,000)
IIC2
4.30
5.58
3.40
4.73
11.03
9.22
4.59
4.98
4.30
4.46
4.92
4.10
3.92
6.66
6.72
7.39
8.15
5.50
12.19
4.94
4.38
4.79
5.00
3.33
7.83
2.94
3.86
4.46
1.18
5.15
5.87
5.69
5.96
8.93
4.29
6.98
3.59
5.02
3.89
2.45
4.81
8.07
3.66
3.02
2.91
9.52
2.72
7.55
2i89
3.32
4.14
9.00
3.94
7.19
2.99
5.70
11.52
5.94
10.95
5.24
3.79
2.11
7.63
4.24
5.96
4.72
Sales/
Employee In
(In $1,000)
IIC3
33.00
30.90
32.60
31.90
34.90
31.10
29.30
30.70
28.60
30.20
32.00
32.20
31.90
32.60
30.90
32.70
30.20
35.80
30.30
31.60
33.70
32.60
31.10
32.00
25.60
33.10
31.30
29.50
36.90
31.20
34.80
30.70
31.20
30.10
29.00
28.50
31.90
29.40
32.40
34.00
28.10
31.10
29.60
35.90
32.20
32.50
31.70
33.20
31.20
31.00
33.30
34.90
32.20
28.90
28.40
34.30
34.30
35.30
37.80
35.10
28.90
32.70
29.70
32.90
31.80
31.10
Slle«/
Employee
in Wholesale
(In $1,000)
IIC4
130.60
1T1.70
116.30
85.70
120.30
162.10
120.70
122.70
143.30
130.70
166.10
173.30
153.60
114.90
155.20
117.60
122.00
172.20
88.50
111.00
110.70
103.70
106.50
101.10
84.90
136.90
136.60
175.20
96.80
170.70
128.90
122.60
181.60
89.60
136.30
167.40
99.90
129.20
183.80
142.30
91.80
120.70
192.10
150.90
135.50
96.50
140.40
164.20
91.80
150.20
114.40
96.40
157.50
100.00
89.90
85.20
90.00
159.20
123.00
133.00
88.40
140.20
93.20
109.40
131.70
110.60
Sales/
En^iloyee
in Selected
Services
(In $1,000)
IIC5
IS. 60
14.60
17.30
13.80
15.20
13.90
15.90
12.40
15.10
14.20
19.10
15.30
15.10
12.50
14.30
14.60
13.80
18.70
11.40
14.90
13.80
14. SO
12.60
14.60
13.90
14.20
14.20
13.60
12.40
13.80
17.10
12.90
12.70
12.60
14.40
15.50
14.10
11.40
24.20
12.80
11.60
13.10
14.20
15.90
15.30
11.60
15.60
15.00
14.20
11.70
17.10
14.10
15.90
13.50
10.90
13.60
14.40
16.70
15.10
15.10
12.40
14.20
13.00
12.70
13.60
14.00
Total Bank
Per Capita
IID
2492.00
1661.00
4413.00
2284.00
1281.00
2189.00
1711.00
1668. (0
4803.00
3356.00
3398.00
1625.00
2936.00
1824.00
3162.00
1260.00
2006.00
2610.00
1886.00
1974.00
1331.00
2321.00
1724.00
4084.00
2162.00
2681.00
2829.00
2081.00
2611.00
2491.00
2198.00
2081.00
2075.00
2228.00
2316.00
2591.00
2681.00
2080.00
8813.00
3218.00
1127.00
2372.00
2014.00
2324.00
2842.00
2104.00
2807.00
2033.00
2676.00
2583.00
2975.00
2039.00
2353.00
1686.00
1592.00
1225.00
1420.00
4258.00
1650.00
2350.00
3065.00
2934.00
2028.00
1711.00
1823.00
1473.00
Central City
and Suburban
Distribution
IIE1
.06
.04
.04
.02
.06
.08
.12
.06
.08
.08
.10
.02
.16
.06
-.06
.08
-.02
.10
-.06
.02
.06
.02
-.06
.10
-.14
-.04
-.04
.00
.04
.02
-.02
.06
-.06
.08
.08
.04
-.06
.04
.06
.16
.04
-.02
-.04
.08
.08
-.02
.02
.00
.00
.04
.08
-.02
.08
-.06
.08
-.02
-.06
.02
.10
-.04
.06
.00
.04
.02
.04
.04
Percent of
Families With
Income Belov
Pov. Level or
$15,000
IIE2
31.30
30.60
29.80
24.10
38.90
35.20
32.90
29.80
36.20
28.20
38.80
30.00
35.10
30.60
32.90
32.90
31.60
39.50
29.90
29.30
30.90
28.00
27.40
38.00
42.20
32.40
30.40
30.40
28.50
29.90
36.60
27.80
33.00
32.40
31.80
33.20
28.90
33.80
38.40
39.80
29.50
28.40
28.00
40.90
32.70
30.20
26.00
29.10
27.10
30.10
36.70
32.60
31.00
27.10
30.90
29.40
31.80
39.30
40.50
35.00
27.70
29.60
24.90
31.10
45.70
27.40
Rate
IIF
4.40
4.40
3.30
2.30
5.40
3.00
3.50
4.20
3.50
4.80
3.50
3. SO
3.50
3.40
3.00
3.80
3.70
5.70
3.40
3.50
4.00
5.70
2.80
2.90
3.00
3.00
3.90
3.30
4.70
3.30
6.20
4.00
4.80
3.70
3.50
3.20
3.30
5.00
3.80
3.70
3.80
3.20
3.00
3.70
3.70
3.90
4.30
6.10
3.90
2.20
3.50
7.20
4.90
4.60
4.20
5.90
6.30
5.80
5.80
8.20
4.20
4.50
3.60
4.20
2.70
5.60
NA
2.10
.80
1.30
.60
3.70
1.50
4.30
1.30
2.70
1.40«
3.60
1.60«
3.30
2.10*
3.40
3.10
.90*
2.30
4.20*
2.70»
3.50
3.00
9.00
3.70
3.70
2.30
5.50
2.30
2.20
1.40
40
80 •
50
70 •
SO
40»
70
90 •
90»
50
4.70
5.40
.40
.90
4. 60
.80
3.50 •
2.10
5.60»
4.00
5.60»
1.50
1.30«
4.40
.50
2.70«
1.20
1.80
2.30
2.80
2.70
3.50
2.60
.70
2.40
233
-------
TABLE A-2
BASIC STATISTICS OF POLITICAL COMPONENT (L)
Local Sun.
cite./
1,000 pop.
housing
with TV
station*/
IA3
cast/voting
IB
Avg.
monthly
earnings
IIA1
Avg.
monthly
earnings
of other
employees
Entrance
salary of
patrolmen
Entrance
salary of
firemen
Total
municipal
employment/
1,000 pop.
IIA5
P(
proi
emp!
1,0(
243.00
95.50
.03
54.90
682.00
515.00
6848.00
6569.00
IS.«0
001 AK9
002 AL*
003 ALL
004 ANA
006 AtL
006 HAL
007 HI»
008 fcOS
009 HU&-
010 CHI
Oil CIN
012 CLF
013 COL
014 DAL
015 DAY
016 HEN
017 LIET
01M Ft>»
01* FOR
0?0 RAW
0?1 (iHA
0?2 GWF
0?) H»w
Oft H0»l
0?b Mllll
026 IN"
0?7 JAC
02B Jff
029 KAN
030 LOS
Oil LI'll
032 fl»
033 WIA
U34 MIL
O3'i "IN
Olti MAS
037 Nt M
mi Nf»
031* Nt *
040 NOfc
04 1 OKL
o<>2 OMA
04J PAT
044 PM]
0»b PHO
0".h Pi 1
047 fMW
0<»H PKO
04<» WIC
050 l-OC
Obi SAC
OS2 ST
053 SAL
054 SAN
055 SAN
056 SAN
057 SAN
05H SAN
05% SIA
060 SHH
Ohl SY*<
Oh2 TA«
063 TOL
064 HAS
065 YOU
752.00
1248.00
1185.00
108.00
1135.00
687.00
736.00
1341.00
1338.00
781.00
664.00
722.00
615.00
669.00
908.00
1122.00
1000.00
84?. 00
70B.OO
421.00
640.00
729.00
P077.00
544.00
561.00
501.00
315.00
325.00
7SS.OO
631.00
977.00
4.15.00
1532.00
74H.OO
1461.00
527.00
509.00
f><.<,.00
2046.00
674.00
863.00
845.00
319.00
H14.00
43B.OO
1421.00
1069.00
1174.00
788.00
744.00
1123.00
1396.00
1059.00
414.00
760.00
383.00
920.00
460.00
1070.00
734.00
1245.00
664.00
528.00
1336.00
1129.00
97.40
97.20
97.00
97.10
96.50
97.00
95.60
96.70
97.60
96.10
97.00
97.20
17.60
95.90
97.70
95.00
97.10
97.50
96.40
96.40
96.70
95.30
96.90
94.40
94.70
<<6.60
95.80
96.40
95.90
95.00
96.40
95.30
94.90
97.30
V6.80
U6.10
95.90
95.20
97.00
95.90
96.40
96.00
97.90
97.00
95.70
97.30
94.70
97.70
95.50
97.50
95.60
95.60
96.30
93.90
95.10
95.00
93.00
45.60
94.60
96.70
97.30
95.80
97.40
96.30
97.40
.68
1.10
.91
.21
1.36
.77
1.89
.72
1.33
.45
.93
.82
1.20
.96
1.05
1.46
.52
.96
1.04
.31
2.22
.82
1.J5
2.86
1.15
1.26
2.83
0.00
.87
.36
1.33
1.68
1.26
.99
l.lh
2.21
1.62
.2>)
.16
1.61
2.02
1.29
0.00
.51
2.68
.66
2.08
1.20
2.31
1.47
1.37
.97
2.b8
l.BS
.43
1.10
.80
.93
1.89
1.50
1.57
.88
1.1S
.69
.93
61.20
79.70
58.00
73.30
50.90
51.80
49.00
64.10 •
64.60
64.00
SB. 50
5H.10
69.50
50.10
53.10
69.40
62.60
60.90
50.10
61.60
70.10
58.60
68.80 •
52.00
53.30
62.10
59.10
52.90
57.90
59.40
56.10
56.90
48.70
62.50
76.90
56.90
46.00
45.10
66.90
39.70
67.30
57. iO
18.90
61.70
56.80
S9.30
68. VO
67.10 •
55. 60
73.30
70.90
61.10
Hi. »0
46.70
57.90
65.10
64.30
67.80
67.80
60.10 •
65.40
61.90
64.70
50.10
61.30
747.00
763.00
637.00
881.00
632.00
741.00
569.00
700.00
850.00
758.00
703.00
702.00
584.00
561.00
677.00
615.00
887.00
717.00
580.00
824.00
630.00
619.00
786.00
0.00
599.00
713.00
578.00
672.00
589.00
965.00
724.00
673.00
910.00
766.00
703.00
695.00
711.00
791.00
803.00
650.00
595.00
644.00
817.00
824.00
761.00
643.00
687.00
696.00
673.00
840.00
766.00
704.00
611.00
559.00
900.00
930.00
840.00
954.00
713.00
741.00
764.00
725.00
696.00
732.00
661.00
487.00
392.00
434.00
671.00
421.00
474.00
383.00
558.00
516.00
589.00
457.00
515.00
473.00
424.00
506.00
492.00
A02.00
446.00
407.00
477.00
542.00
407.00
568.00
612.00
437.00
393.00
462.00
441.00
466.00
694.00
310.00
390.00
506.00
575.00
S72.00
469.00
39H.OO
659.00
545.00
360.00
394.00
568.00
512.00
526.00
359.00
448.00
577.00
448.00
451.00
503.00
620.00
469.00
466.00
409.00
596.00
644.00
6S1.00
668.00
580.00
492.00
438.00
388.00
519.00
536.00
470.00
0278.00
6800.00
6830.00
9162.00
6760.00
7452.00
6900.00 •
8030.00
7400.00
9840.00
8636.00
8432.00
7436.00
690>0.00
7748.00
6600.00
6000.00
7816.00
7590.00
B620.00
8133.00
6612.00
7865.00
6660.00
7800.00
7000.00
6564.00
10112.00
7218.00
9564.00
6900.00 •
6740.00
7896.00
7950.00
9000,00
5970.00
6360.00
9499.00
6900.00 •
6144.00
6450.00
7452.00
8350,00
8478.00
7224.00
8463.00
8955.00
6932.00
7020.00
6864.00
9306.00
7657.00
6168.00
6708.00
8472.00
8988.00
10476.00
9546.00
8856.00
7100.00
7030.00
7043.00
8070.00
8000.00
7186.00
8278.00
7055.00
6839.00
8940.00
6760.00
7824.00
6758.00 •
7718.00
7400.00
9840.00
8636.00
8430.00
7436.00
6900.00
7748.00
6600.00
8000.00
7442.00
7032.00
7939.00
8083.00
5712.00
7865.00
6660.00
7800.00
6800.00
6252.00
10045.00
7044.00
9564.00
6548.00
6765.00
7836.00
7950.00
8580.00
5970.00
6360.00
9499.00
6758.00 •
6144.00
6450.00
7452.00
8350.00
8478.00
6612.00
8463.00
8091.00
7436.00
6396.00
6864.00
9210.00
7463.00
6168.00
6295.00
8700.00
8664.00
11196.00
9324.00
8850.00
7072.00
7030.00
6321.00
8070.00
8000.00
7186.00
9.50
25.30
9.00
8.50
13.90
42.80
11.70
35.70
26.20
12.40
27.90
18.70
9.40
11.80
12.50
17.80
17.90
13.50
9.30
8.00
9.10
10.50
34.30
20.10
8.00
5.70
11'. 60
15.50
10.80
14.70
17.30
37.80
12.00
13.40
11.80
32.00
16.20
45.50
37.20
28.90
8.90
6.20
20.00
17.40
9.10
14.60
11.30
26.20
32.60
33.40
11.50
20.30
9.70
10.70
10.80
7.30
27.90
5.50
18.30
28.10
26.00
14.90
9.50
57.20
10. If
234
-------
TABLE A-2 (Concluded)
US
001 AUK
00<- AlH
1)03 ILL
004 ANA
005 All
OOh MIL
007 Ml*
OOB «OS
00V MUF
010 CHI
Oil CIN
012 CLF
013 UU.
014 0*1
015 DAY
Olft DEN
017 DEI
018 H)R
019 (OR
0?0 GArt
021 GRA
022 Ghf
023 HAP
024 WON
02S HOU
02b IND
027 JAC
028 JtP
029 KAN
030 LOS
031 LOU
032 «FM
033 MIA
034 111
Oib «I1N
036 MAS
037 Mt«
038 HI*
039 NE»
040 NOH
041 OKL
042 DM*
043 PAT
04* OH I
04S °HO
046 »>1T
047 KIR
046 CHO
049 SIC
OSO ROC
051 SAC
052 ST
053 SAL
OS4 SAM
OSS SAN
OS6 SAN
OS7 SAN
OSS SAN
OS9 SEA
060 SP»
061 SYB
062 IAU
063 IOL
064 «AS
065 YOU
Fire
protection
employment/
1.000 pop.
IIA7
1.40
1.30
2.20
l.SO
1.10
1.80
2.40
1.90
3.20
2.70
1.50
2.00
1.80
1.40
1.50
2.00
l.SO
1.30
1.70
1.40
1.60
l.SO
l.SO
3.10
2.40
l.SO
1.20
1.10
3,00
1.80
1.20
1.60
2.20
2.10
1.50
1.30
1.30
1.60
1.80
2.80
1.40
1.70
1.30
?.30
1.60
.90
2.20
1.90
2.60
2.10
2.40
1.70
1.90
l.SO
1.00
1.80
.90
2.50
1.10
1.90
2.80
2. SO
2.20
1.50
2.00
1.90
Insured-
unemployment
rates
UA8
1.40
1.90
2.40
1.40
4.20
1.10
2.50
3.10
3.50
3.70
1.80
1.70
2.00
1.40
.90
1.80
.60
4.40
2.10 •
2.00
l.SO
4.40
1.40
3.00
2.80
.50
1.90
,90
5.70
2.20
4.60
1.80
1.50
2.90
2.20
1.70
1.30
2.50
3.20
3.60
1.20
1.50
1.30
4.40
2.60
4.10
2.40
4.20
4.70
.40
2.60
6.20
3.30
2.50
1.40
4.80
4.70
3.90
4.50
8.40
4.60
3.40
1.60
2.60
.90
3.30
Violent
Crime
rate/
100.000 pop.
IIB1
397.70
275.00
133.70
133.00
261.60
553.90
9bh.60
448.80
350.40 •
300.00
671.70
297.60
483.00
336,10
563.20
299.40 •
493.60
621.10
465.10
272.00
516.70
231.80
602.00
238.20 •
14H.SO
459.90
292.60
799.10
440.50
530.30
653.00
340.00
512.80
669.30
138.60
325.30
557.20
658.90
1357.10
638.90
544.80
319.90
372.20
282.90
441.10
550.90
284.80
427.20
264.70 •
565.20
195.00
366.70
559.50
243.80
419.40
421.20
266.20
643.00
271.50
317.30
261.80 •
146.40
566.50
323.30
669.30
277.70
Property
Crime
rate/
100,000 pop.
IIB2
2431.80
2434.20
1518.20
1457.10
3676.70
3470.70
3095.10
2421.70
3053.60*
2375.80
2241.80
2346.20
2459.90
2912.80
3117.10
2061.70*
4610.60
3997.10
3869.20
2520.50
3689.30
2080.30
2216.90
2015.30*
2976.00
3109.70
2367.80
3522.00
2696.00
2889.10
4578.70
2703.10
3538.60
4282.10
2111.80
3172.30
2735.40
3275.10
3737.10
3064.20
2609.30
2766.50
2751.50
2240.90
2146.90
4101.10
1603.70
3931.80
3133.50 •
3019.40
1905.00
4048.90
3027.60
3197.00
3141.80
3967.10
3083.80
4362.60
3464.70
3554.60
3418.00 •
1529.50
3415,50
2587.20
2811.00
1788.10
Local
fOVt.
revenue
per ciplta
1IB3
329.6*.
288.02
344.60
240.99
425.18
301.42
352.05
228.04
426.30
395.73
340.32
309.19
321.97
2S9.97
262.27
282.10
350.79
378.63
245.47
241.48
336.20
298.12
303.94
354.90
186.45
256.75
325.96
330.59
318.70
303.61
514.00
275.36
389.79
344.55
407.36
385.46
321.94
245.74
633.53
343.85
273.78
273.70
407.66
280.81
273.35
394.02
279.69
331.16
251.76
271.85
426.09
527.17
278.49
280.83
262.24
479.00
425.37
554.82
484.95
408.41
340.23
395.19
243.39
279.19
39*. 46
239.55
t of
from
federal
govt.
1IBA
2.70
1.20
2.80
2.10
.90
2.10
2.00
2.30
2.90
1.80
3.20
6.00
2.00
2.70
.90
3.50
2.50
2.50
1.30
1.90
3.10
.50
3.80
2.50
5.40
1.20
.50
1.50
2.60
3.70
.70
8.00
1.40
5.20
1.00
1.60
5.40
5. SO
2.10
3.00
6.60
s.eo
2.90
1.10
4.60
2.80
4.80
l.BO
4.40
1.60
2.00
2.40
2.40
3.60
8.30
2.20
4.60
2.20
2.20
2.80
2.40
2.50
2.60
1.90
17.40
l.SO
Per capita
expend .
on public
welfare
net
11.88
18.03
30.57
9.81
20.54
3.68
33.25
1.99
54.77
40.46
9.09
15.58
17.07
16.82
1.37
14.01
35.60
10.35
2.91
1.09
26.20
11.85
21.72
6.12
0.00
1.16
13.00
.30
20.03
5.92
59.78
6.05
.69
3.11
25.09
46.03
1.86
.66
70.87
31.90
18.32
1.05
15.10
12.43
9.16
.05
2.57
1.39
9.92
25.16
32.50
66.33
2.26
.10
.45
67.37
42.41
62.29
50.85
.08
40.16
40.86
3.96
15.83
It.SZ
11.96
monthly
retiree
benefits
iic;
112.00
148.00
141.00
142.00
139.00
126.00
136.00
130.00
140.00*
145.00
146.00
134.00
145.00
133.00
127.00
138.00
111.00
150.00
141.00
12S.OO
151.00
141.00
124.00
149.00*
111.00
112.00
140.00
121.00
143.00
137.00
136.00
133.00
118.00
135.00
146.00
140.00
121.00
127.00
146.00
147.00
120.00
127.00
135.00
147.00
142.00
137.00
148.00
136.00
136.00*
133.00
146.00
129.00
119.00
139.00
115.00
132.00
111.00
141.00
136.00
142.00
138.00 •
143.00
133.00
U».»0
146.00
Avg. monthly
to families
w/de pendent
children
IIC3
190.00
156.00
211.00
215.00
203.00
102.00
162.00
59.00
257.00*
231.00
241.00
147.00
180.00
159.00
117.00
152.00
179.00
244.00
94.00
117.00
152.00
231.00
124.00
264.00*
296.00
123.00
153.00
87.00
267.00
134.00
222.00
128.00
107.00
104.00
272.00
259.00
106.00
96.00
248.00
275.00
193.00
144.00
164.00
264.00
255.00
129.00
237.00
176.00
230.00*
189.00
269.00
226.00
152.00
181.00
122.00
226.00
222.00
226.00
227.00
230.00
228.00*
231.00
89.00
161.00
191.00
156.00
235
-------
TABLE A-3
BASIC STATISTICS OF ENVIRONMENTAL COMPONENT (Li
1 AKH
2 ALB
3 ALL
4 ANA
5 ATL
6 BAL
7 BIR
B HOS
-------
TABLE A-3 (Concluded)
1 AKR
2 ALB
3 ALL
* ANA
5 ATL
6 BAL
7 SIR
8 HOS
9 bUF
10 CHI
11 CIN
12 CLl
13 COL
14 OAL
15 DAY
16 OEN
17 DET
18 FOR
19 FOR
20 GAR
21 GHA
22 SHE
23 HAR
2* MON
25 HOU
26 INO
27 JAC
28 JER
29 KAN
30 LOS
31 LOU
32 MEM
33 MIA
34 MIL
35 MIN
36 NAS
37 NEW
38 NEW
39 NEW
40 NOR
41 OKL
42 OHA
43 PAT
44 PHI
45 PHO
46 PIT
47 POR
48 PRO
49 R1C
50 ROC
51 SAC
52 STL
53 SAL
54 SAN
55 SAN
56 SAN
57 SAN
58 SAN
59 SEA
60 SPR
61 SYR
62 TAM
63 TOL
64 WAS
65 YOU
Mean
Annual
Inversion
Frequency
IIA1
27.50
32.50
27.50
37.50
37.50
22.50
37.50
27.50
22.50
32.50
32.50
22.50
27.50
27.50
27.50
37.50
32.50
12.50
27.50
32.50
32.50
42.50
27.50
27.50
22.50
32.50
32.50
22.50
37.50
37.50
32.50
37.50
7.50
32.50
32.50
37.50
27.50
22.50
27.50
22.50
37.50
37.50
27.50
27.50
43.50
32.50
37.50
22.50
27.50
22.50
42.50
37.50
42.50
27.50
42.50
37.50
37.50
37.50
27.50
27.50
27.50
27.50
27.50
27.50
27.50
Possible
Annual
Sunshine
Days
IIA2
52.00 •
54.00
57.00*
73.00
61.00
58.00
58.00
60.00
53.00
57.00
54.00
52.00
55.00
65.00
57.00
70.00
54.00
73.00 •
65.00
57.00
51.00
62.00
57.00
69.00
59.00
59.00
61.00
59.00 •
64.00
73.00
58.00
65.00
73.00 •
56.00
58.00
58.00
60.00 •
59.00
59.00 •
63.00
67.00
62.00
59.00 •
58.00
66.00
52.00
47.00
57.00
60.00
55.00
79.00
59.00
69.00
62.00
73.00 •
67.00
67.00 •
67.00
48.00
57.00 •
51.00
66.00
57.00
57.00
52.00 •
No. of
Days With
Thunder-
storms
IIA3
40.00
24.00
32.00
1.00*
58.00
24.00
84.00
17.00
36.00
47.00
58.00
40.00
46.00
48.00
48.00
38.00
33.00
71.00 •
52.00
47.00 •
36.00
45.00
28.00
8.00
72.00
49.00
85.00
33.00 •
54.00
1.00
62.00
62.00
71.00
29.00
40.00
58.00
75.00
33.00
30.00
36.00
64.00
36.00
30.00*
25.00
20.00
38.00
7.00
14.00
40.00
25.00
8.00
52.00
41.00
37.00
1.00*
3.00
2.00 •
2.00
4.00
28.00 •
33.00
91.00
47.00
26.00
30.00
No. of
Days With
Temp. 90°
or Above
IIA4
10.00
17.00
17.00
21.00 •
16.00
27.00
30.00
19.00
2.00
29.00
18.00
11.00
17.00
86.00
21.00
33.00
14.00
13.00 •
82.00
29.00 •
14.00
21.00
30.00
10.00
68.00
17.00
81.00
18.00 •
26.00
21.00
27.00
57.00
13.00
15.00
13.00
37.00
70.00
18.00
31.00
29.00
53.00
38.00
31.00 •
28.00
172.00
17.00
18.00
17.00
32.00
25.00
78.00
42.00
58.00
96.00
21.00 •
5.00
6.00 •
6.00
1.00
30.00 •
15.00
96.00
16.00
37.00
6.00
No. of
Days With
Temp. 32'
or Below
IIA5
105.00
127.00
101.00
0.00 •
55.00
84.00
58.00
76.00
111.00
100.00
94.00
94.00
99.00
27.00
101.00
158.00
113.00
0.00 •
20.00
100.00 •
124.00
92.00
113.00
0.00
24.00
100.00
15.00
60.00 •
90.00
0.00
77.00
51.00
0.00
117.00
138.00
59.00
18.00
60.00
57.00
48.00
68.00
119.00
57.00 •
76.00
3.00
101.00
27.00
95.00
80.00
106.00
14.00
88.00
116.00
29.00
0.00 •
0.00
2.00 •
2.00
17.00
113.00 •
111.00
6.00
122.00
103.00
115.00
Park and
Recreation
Acres/1,000
Pop.
IIB1
34.80
16.00
19.80
5.90
5.60
4.80
16.20
3.90
5.10
4.70
11.90
6.50
30.90
17.70
12.90
48.10
13.10
3.80
11.70
21.40
9.10
3.20
11.00
9.30
6.10
9.00
5.30
1.00
11.60
4.80
7.10
27.10
447.20
11.10
14.20
20.20
1.10
7.60
5.00
8.70
9.40
15.20
5.40
6.80
116.30
8.30
11.60
25.00
6.50
29.00
130.10
2.30
10.40
3.20
45.10
18.90
23.10
25.50
9.20
15.90
7.40
14.00
5.50
30.00
41.50
Miles of
Trails/
100,000
Pop.
IIB2
148.70
22.10
165.40
51.40
56.80
11.50
18.90
B9.60
42.20
34.10
119.80
112.40
SI. 30
70.60
123.50
205.20
51.10
9.60
68.20
47.30
243.00
19.90
63.30
9.50
27.70
93.70
5.70
3.30
S6.60
156.30
72.60
80.50
128.50
74.10
236.50
175.60
3.80
30.00
80.20
13.20
352.60
88.90
14.70
112.30
263.40
41.20
659.10
199.80
54.10
231.00
1968.80
37.70
363.80
67.10
955.40
134.80
206.10
200.00
643.50
701.90
51.90
28.60
14.40
197.10
33.60
237
-------
TABLE A-4
BASIC STATISTICS OF HEALTH AND EDUCATION COMPONENT
us
1 AKR
2 ALB
3 ALL
4 ANA
5 ATL
6 BAL
7 BIR
8 BOS
9 BUF
10 CHI
11 CIN
12 CLE
13 COL
14 DAL
15 DAY
16 DEN
17 DET
IB FOR
19 FOR
20 GAR
21 GRA
22 ORE
23 HAR
24 HON
25 HOU
26 INO
27 JAC
28 JER
29 KAN
30 LOS
31 LOU
32 MEM
33 MIA
3* MIL
35 MIN
36 NAS
37 NEW
38 NEW
39 NEK
40 NOR
41 OKL
42 OMA
43 PAT
44 PHI
45 PHO
46 PIT
47 POR
48 PRO
49 RIC
50 ROC
51 SAC
52 STL
53 SAL
54 SAN
55 SAN
56 SAN
57 SAN
58 SAN
59 SEA
60 SPR
61 SYR
62 TAM
63 TOL
64 HAS
65 YOU
Infant
Mortality
Rate/ 1,000
Live Births
IA1
21.20
20.60
19.90
18.00
20.60
22.20
23.00
23.00
20.10*
22.20
24.40
20.30
21.40
20.60
22.10
19.80
18.00
22.20
23.00
24.20
26.80
18.90
26.20
19.00*
18.40
23.10
24.60
22.10
23.50
21.20
18.90
21.40
23.40
21.60
19.20
18.50
21.60
22.60
21.60
23.00
22.90
20.10
20.10
IB. 10
23.40
19.30
22^00
19.00
22.50 •
25.50
20.40
20.30
21.80
18.00
23.00
19.50
19.90
16.90
15.50
17.60
21.20 •
17.90
24.50
20.90
19.90
19.80
Death Rate/
1,000 pop.
IA2
9.50
8.50
10.90
10.40
6.00
7.80
9.40
10.30
8.10 •
10.10
9.70
10.00
9.60
8.20
7.70
7.80
7,40
8.60
11.10
7.90
8.20
8.20
8.40
8.50 •
4. BO
7.00
9.10
9.00
12.20
9.20
9.00
9.50
9.10
10.50
8.90
7.70
9.10
V.70
10.50
9.80
7.70
8.00
8.30
9.00
10.10
7.90
10.70
9.90
10.50 •
9.80
9.00
7.80
10.00
6.20
7.50
8.90
7.50
9.10
5.70
8.50
10.00 •
9.30
13.70
9.80
7.00
9.90
Median
Schools
Yeara
Completed
IB1
12.10
12.20
12.20
11.70
12.60
12.10
11.30
11.40
12.40
12.00
12.10
11.80
12.10
12.30
12.20
12.20
12.50
12.10
12.20
12.10
12.00
12.10
11.10
12.30
12.40
12.10
12.20
12.00
10.20
12.30
12.40
11.60
11.90
12.10
12.20
12.40
11.90
11.40
12.10
12.20
11.80
12.30
12.30
12.20
12.00
12.30
12.10
12.40
11.50
11.70
12.20
12.40
11.70
12.50
11.50
12.20
12.40
12.50
12.60
12.50
12.10
12.20
12.00
12.00
12.60
12.10
I of Persons,
25+ , Completed
4yrs, HlghScool
or more IB2
52.30
55.60
56.10
47.80
70.50
53.40
44.60
45.40
64.40
50.40
53.90
46.40
54.60
60.70
54.80
56.20
67.40
52.10
55.40
52.00
50.00
54.00
42.40
59.10
66.00
51.70
56.00
51.60
36.30
60.10
62.00
46.90
49.20
51.90
56.80
66.10
49.00
45.80
51.80
55.10
48.30
61.00
62.70
54.60
50.60
60.10
53.40
62.90
45.90
47.10
56.10
65.10
48.00
68.50
46.80
57.40
65.30
66.10
69.00
67.60
53.50
57.80
51.40
51.70
68.50
52.10
I of Males
16-21,
not High
School
Graduates
183
15.20
10.20
8.90
10.10
12.10
18.40
19.60
18.90
9.90
11.20
16.10
16.60
12.70
11.20
20.30
12.00
11.70
16.40
18.00
16.00
13.90
11.00
18.20
11.40
13.60
19.90
19.50
17.70
18.00
13.60
13.30
18.40
17.80
15.30
10.70
7.40
19.30
21.90
15.60
13.00
18.40
11.30
10.50
10.40
15.10
15.50
8.10
10.30
17.20
16.70
12.00
7.10
14.30
11.10
15.40
14.90
19.60
10.30
8.50
9.50
13.60
10.60
17.00
10.60
14.20
9.50
1 of pop.
3-34
Enrolled
in Schools
IB4
54.30
56.90
58.10
55.60
57.70
50.40
53.80
54.20
57.60
58.50
54.60
55.10
55.30
54.40
49.40
53.90
55.50
56.20
54.00
51.60
56.40
58.20
52.30
56.90
51.30
51.80
52.90
50.00
50.70
53.80
54.80
53.10
52.60
55.40
56.80
55.00
52.30
54.70
53.10
56.10
45.30
55.80
55.60
57.10
55.20
56.00
57.60
55.30
55.10
53.50
55.40
59.40
56.20
59.10
52.30
56.20
50.80
54.60
58.10
55.50
57.50
58.60
55.10
57.10
51.50
57.30
238
-------
TABLE A-4 (Concluded)
us
AKrt
ALB
3 ALL
ANA
ATL
6 BAL
7 BIR
8 HOS
9 BUF
10 CHI
11 CIN
12 CLE
13 COL
1* DAL
15 DAY
16 DEN
17 OET
18 FOK
19 FOR
20 GAR
21 GHA
22 GRE
23 HAH
2* HON
25 HOU
26 I NO
27 JAC
2b JfcR
29 KAN
30 LOS
31 LOU
32 MtM
33 MIA
34 MIL
35 M1N
36 NAS
37 NEW
38 NEW
39 NEW
40 NOR
41 OKL
42 OMA
43 PAT
44 PHI
45 PHO
46 PIT
47 POR
48 PRO'
49 RIC
50 HOC
51 SAC
52 STL
53 SAL
54 SAN
55 SAN
56 SAN
57 SAN
58 SAN
59 SEA
60 SPR
61 SYR
62 TAM
63 TOL
64 WAS
65 YOU
Dentists/
100,000
pop. IIA1
59.50
Hospital
Beds/
100,000
pop. IIA2
414.90
Hospital
Occupancy
Rates IIA3
79.80
Physicians/
100,000
pop. IIA4
153.80
Per Capita Per Capita
Local Gov't Local Gov't
Expend, on Expend, on
Health IIA5 Educ. IIB1
48.30
58.50
55.60
62.20
51.90
47.10
58.20
82.50 •
69.40
67.50
43.20
66.90
69.40
57.00
43.50
69.20
57.40
80.10
41.20
41.20
57.50
41.60
65.00 •
69.50
52.40
63.00
39.10
56.80
64.50
66.00
53.20
65.70
66.50
71.10
79.60
58.40
55.70
96.00
78.90
41.00
47.60
64.20
74.40
62.30
57.20
62.10
93.50
55.20 .
66.00
68.90
58.80
54.20
65.10
41.60
55.40
68.60
89.90
72.80
84.70
59.30 «
54.60
54.60
46.20
66.40
47.00
329.60
447.60
363.90
258.80
321.00
377.70
493.30
466.80 >
493.80
416.90
370.70
418.30
352.00
346.50
332.40
448.00
339.50
341.90
365.20
336.10
309.50
415.10
383.50 •
230.60
431.20
412.70
365.70
393.30
470.00
368.50
418.80
472.00
458.90
444.40
547.20
539.60
507.20
462.50
436.90
281.40
405.80
694.60
336.50
429.80
351.20
494.00
415.20
408.00 '
506.30
290.60
414.30
496.40
334.10
332.30
364.40
266.70
444.30
312.30
318.70
437.50 •
291.40
394.50
464.60
306.90
366.80
87.70
87.30
82.30
71.00
83.60
79.60
82.70
80.00 •
85.20
84.10
79.10
83.70
88.80
78.70
84.20
83.20
B4.70
82.30
74.80
81.50
87.00
81.30
84.20 •
82.00
78.80
83.70
83.80
81.70
83.70
78.00
84.50
88.90
61.70
99.00
77.00
61.70
76.40
83.80
84.30
92.50
78.70
73.60
79.40
83.20
78.70
87.40
78.90
86.40 .
84.00
86.80
78.10
82.90
80.20
93.90
71.10
79.20
76.50
77.70
77.20
84.40 •
76.10
81.00
86.80
81.30
64.60
127.10
213.10
127.70
164.60
174.00
243.40
176.30
274.00
185.10
176.60
169.00
209.40
174.60
161.40
107.60
242.60
146.00
185.60
99.20
83.60
125.60
145.10
221.10
169.00
159.50
166.20
131.20
133.90
156.00
207.00
152.30
185.90
266.40
156.30
176.60
223.40
223.00
286.20
196.10
105.60
183.50
164.00
163.00
206.80
183.70
154.10
196.70
190.70
232.10
216.40
178.50
160.40
197.60
149.40
155.90
202.90
272.40
233.10
206.10
131.70
186.00
139.40
139.90
212.50
125.40
2.96
US.69
7. of Persons,
25+, Completed
4 yrs. College
or more IIB2
10.70
2.62
4.96
1.05
2.30
2.94
5.45
3.07
2.92
5.66
2.51
2.34
2.22
2.06
1.39
2.19
4.20
2.76
.69
1.74
1.77
2.49
3.23
2.89
0.00
2.76
.18
3.29
3.48
2.13
5.70
3.18
3.46
.49
4.51
2.47
3.27
2.61
8.82
3.27
2.10
1.31
2.78
2.74
2.20
3.53
1.91
4.42
.88
.66
8.23
4.73
2.86
2.58
2.03
3.72
4.42
7.83
7.90
3.01
2.41
6.13
1.67
3.87
8.20
1.14
153.62
188.46
131.74
213.39
141.30
147.36
102.60
130.66
178.37
127.76
141.78
139.72
127.60
123.59
141.51
151.94
178.38
119.33
128.53
175.52
145.55
130.77
182.77
0.00
142.56
156.00
120.12
90.35
135.28
171.50
144.58
117.64
135.42
142.39
174.67
122.62
111.71
180.20
148.95
121.02
119.36
133.53
139.42
133.69
168.15
139.16
162.61
118.37
122.21
225.75
205.37
142.28
173.95
107.14
214.28
169.29
187.01
224.00
179.35
118.91
196.85
114.25
142.99
176.02
121.40
10.80
12.60
8.20
15.80
14.30
10.30
8.90
15.80
9.60
11.70
10.60
10.90
14.00
13.90
11.00
17.30
9.50
9.70
11.40
6.90
9.70
11.00
14.80
15.50
13.90
10.40
8.90
5.60
11.60
12.70
9.00
9.60
10.80
11.20
14.80
11.10
11.30
12.40
14.20
9.30
13.60
11.80
13.20
10.70
12.80
9.60
12.80
8.80
12.50
13.30
13.30
10.10
15.00
10.20
9.80
14.00
16.80
19.50
15.90
9.70
13.20
9.40
8.90
23.40
6.90
239
-------
TABLE A-5
BASIC STATISTICS OF SOCIAL COMPONENT (L)
AKR
ALB
ALL
>N>
ATI
BAL
B1R
BOS
BUF
10 CHJ
11 CIN
1? CLE
13 COL
1* DAL
15 DAT
!<- OfN
17 HIT
10 FOR
19 FOR
20 GAR
31 b«A
22 GRE
23 H«f
24 MON
25 MOU
26 1ND
27 J*C
28 JEH
2* KAN
30 LOS
31 LOU
32 MEM
33 MIA
34 MIL
35 MIN
36 NAS
37 NEK
38 N£.
31 l»t«
40 NOW
41 OKL
42 OMt
43 PAT
44 PHI
45 PHO
46 PIT
47 POD
4B PMO
49 MIC
50 ROC
51 SAC
52 STL
S3 SAL
54 SAN
55 SAN
56 SAN
57 SAN
58 SAN
59 SEA
60 SPR
61 SYR
62 TAM
63 TOL
64 *AS
65 YOU
Labor Force
Participation
Kate (1)
1A1
66.00
66.50
68.80
70.60
87.40
70.40
66.70
63.90
70.90
66.90
70.20
67.40
66.90
67.40
72.60
66.90
60.10
46.70
67.10
69.50
65.20
69.90
71.90
60.20
59.70
68.20
70.50
61.60
70.10
72.30
69.00
68.40
64.50
69.60
72.00
73.60
66.70
63.00
67.00
69.90
49.70
69.10
66.50
71.10
67.10
66.10
62.20
70.30
71.10
70.10
70.70
62.90
66.30
67.70
56.60
60.40
53.70
66.00
66.70
69.70
69.00
67.20
64.70
68.10
67.10
65.70
X of
Labor Force
Employed
95.60
95.60
96.70
97.70
94.60
97.00
96.50
95.80
96.50
95.20
96.50
96.20
96.50
96.60
97.00
96.20
96.30
94.30
96.60
96.50
96.00
94.30
97.20
97.10
97.00
97.00
96.10
96.70
95.30
96.70
93.60
96.00
95.20
96.30
96.50
96.80
96.70
95.00
96.20
96.30
96.20
96.80
97.00
96.30
96.30
96.10
95.70
93.90
96.10
97.80
96. SO
92.80
95.10
95.40
95.60
94.10
93.70
94.20
94.20
91.80
95.80
95.50
96.40
95.00
97.30
94.60
Mean Incone
Per really
Meatier
1^3
3092.00
3361.00
3402.00
3357.00
3796.00
3415.00
3264.00
2728.00
3665,00
3315.00
3730.00
3174,00
3609.00
3334.00
3478.00
3473.00
3427.00
3657.00
3655.00
3252.00
3118.00
3184.00
3071,00
3860.00
3391.00
3218.00
3353,00
2792.00
3094.00
3413.00
3727.00
3126.00
2664.00
3385.00
3450.00
3568.00
3075.00
2736.00
3788.00
3900.00
2732,00
3187.00
3117.00
4172.00
3360.00
3156.00
3166.00
3472.00
3149,00
3294.00
3651.00
3276.00
3265.00
2867.00
2464.00
3006.00
3330.00
3969.00
3750.00
3759.00
3241.00
3216.00
3018,00
3394,00
4089.00
3144.00
X of
Children
Under 18
Living With
Both Parenta
IA4
S2.70
86.00
86.90
68.40
86.00
80.50
77.50
77.40
8S.60
65.30
62.00
84.20
83.80
82.40
81.80
64.90
65.00
63.10
81.60
84.00
64.60
87.50
81.00
85.90
83.70
82.60
83.50
75.00
79.40
83.70
78.60
82.30
73.10
78.50
66.30
88.60
79.70
76.10
78.80
81.50
75.90
81.80
85.60
88.30
81.50
63.40
86.50
84.90
66.10
78.50
86.50
82.10
82.10
87.90
60.30
81.60
79.40
60.40
85.80
65.60
65.10
85.70
79.20
65.30
81.70
86.70
X of
Merrled
Couplet
Without Own
Botieehold
IA.5
1.30
1.20
1.10
1.70
1.00
1.40
1.90
1.70
1.30
1.10
1.30
1.00
1.20
1.00
1.20
.90
1.00
1.50
1.50
1.10
1.30
.60
1.60
1.00
4.70
1.20
.90
1.60
1.40
.90
1.10
1.30
1.80
3.00
.70
.70
1.50
1.90
1.50
l.SO
1.40
.70
.80
1.60
l.eo
1.20
1.40
.70
1.20
1.90
1.30
.80
1.00
.80
2.00
.90
.90
1.00
1.10
.80
1.00
1.20
1.30
1.10
1.20
1.70
Per Caplte
Locel Gov't
Expend, on
Educetion
TB1
145.69
153.6?
188.46
131.74
213.39
141.30
147.36
102.60
130.66
178.37
127.76
141.76
139.72
127.60
123.59
141.51
151.94
178.36
119.33
128.53
175.52
145. 55
130.77
182.77
0.00
1*2.56
156.00
120.12
90. 35
135.26
171.50
144.58
117.64
135.42
142.39
174.67
122.62
111.71
180.20
148.95
121.02
119.36
133.53
139.42
133.69
168.15
129.16
162.61
116.37
122.21
225.75
205.37
142.26
173. 9S
107.14
214.26
169.29
187.01
224.00
179. 3b
116.91
196.65
114.25
142.99
176.02
121.40
X of
Pereona
25+,
Completed
It yra High
School or Hare
IB2
52.30
55.60
56.10
47.80
70.50
53.40
44.60
45.40
64.40
50.40
53.90
48.40
S4.60
60.70
54.60
56.20
67.40
S2.10
55.40
52. 00
50. CO
54.00
42.40
59.10
66.00
51. TO
56.00
51.60
36.30
60.10
62.00
46.90
49.20
51. '10
56.60
66. 10
49.00
45.80
51.30
S5.10
46.30
61. DO
62.70
54.80
50.60
60.10
53.40
62.90
45.90
47.10
56.10
65.10
48.00
68.50
46.80
57.40
65.30
66.10
69.00
67.60
53.50
57.80
51.40
51.70
68.50
52.10
X of
Nalea, 16-64
Leae Than
15 yrt School
But Vocational
Training
IB3a
28.70
30.70
30.30
29.50
37.30
29.50
30.10
26.10
33.60
31.60
31.90
27.90
31.70
30.20
31.70
31.10
36.50
30.80
35.30
34.60
27.40
26.10
24.90
35.10
35.50
30.60
26.90
35.20
26.90
33.60
34.80
27.20
29.60
34.00
34.70
34.60
27.60
31.70
30.10
30.80
37.50
32.40
32.30
33.00
33.50
35.70
30.10
35.90
29.00
26.30
31.60
36.60
30.90
34.00
31.30
34.90
39.70
36.50
37.00
38.60
34.50
30.20
35.30
29.60
36.30
25.40
X of Females
16-64
Less Then
15 yrs School
But Vocetlonal
Training
IB3b
21.90
22.70
24.60
19.30
27.70
24.90
23.10
20.60
26.00
25.20
24.90
23.00
25.20
23.20
24.60
23.20
2B.30
22.90
24.40
25.20
20.90
IV. SO
20.00
26.40
27. HO
2S.BO
22.10
24.30
22. MO
2b.60
29.00
• 1 . li II
*36.00
6V 1.00
S6/e. 00
*H 1 .Oil
*P 1 . 00
6rt?. no
* i- 1 . o o
S8H. 00
b3H.OO
S3*. 00
46H. 00
622.00
»98. on
64H. 00
S?2.00
bVH.OO
bB2.00
564.00
MS. 00
M».00
"•61.00
4bb.OO
665.00
S»,0.0<>
49H.OO
577.00
240
-------
TABLE A-5 (Continued)
us
1 AKR
2 ALB
3 ALL
4 ANA
5 ATL
<> BAL
7 HIM
8 MOS
9 pur
10 CHI
11 CIN
12 CLE
13 COL
14 DAL
Ib DAY
16 DEN
17 [>ET
18 I-OR
19 FOR
20 6AR
21 (tRA
22 GRE
23 HAH
24 riON
25 HOU
26 IND
27 JAC
28 JEH
29 KAN
30 LOS
31 LOU
32 MtM
33 MIA
34 MIL
35 MIN
36 NAS
37 NEW
38 NEW
39 NEW
40 NOR
41 OKL
42 DMA
43 PAT
44 PHI
45 PHO
46 PIT
47 POR
48 PRO
49 RIC
50 ROC
51 SAC
52 STL
53 SAL
54 SAN
55 SAN
56 SAN
57 SAN
58 SAM
59 SEA
60 SPR
61 SYR
62 TAM
63 TOL
64 WAS
65 YOU
Motorcycle
Registrations/
1,000 pop.
IClb
16.00
15.00
5.00
12.00 •
35.00
16.00
7.00
15.00
10.00 •
6.00
10.00
13.00
10.00
14.00
19.00
16.00
24.00
17.00
22.00
24.00
13.00
26.00
16.00 •
10.00 •
13.00
17.00
17.00
21.00
10.00 •
21.00 •
28.00
9.00
12.00
14.00
10.00
20.00
13.00
20.00 •
3.00
10.00 •
16.00 •
29.00
22.00
10.00 •
9.00
25.00
12.00
28.00
13.00 •
16.00 •
7.00
35.00
21.00 •
35.00
13.00
36.00
33.00
25.00
29.00
20.00
10.00 •
7.00
15.00
16.00
16.00 •
13.00
X of
Households
With One
or Mora
Automobiles
IClc
82.50
88.80
82.00
85.50
94.50
85.70
76.70
80.80
76.00
81.00
75.60
81.50
82.90
85.70
89.00
88.80
88.70
85.20
91.60
90.80
84.70
89.40
85.10
85.30
89.20
88.40
86.00
83.30
59.10
85.50
84.90
83.30
78.70
80.40
82.20
85.80
84.70
73.60
55.10
78.40
81.40
89.80
84.80
86.10
76.70
91.20
79.50
86.20
84.00
80.10
85.70
89.40
82.00
90.00
85.80
90.50
89.00
80.70
93.10
86.60
82.20
84.40
85.00
87.60
81.50
88.30
Local Sunday
Newspaper
Circ. /
1,000 pop.
IC2a
243.00
752.00
1248.00
1185.00
108.00
1135.00
687.00
736.00
1341.00
1338.00
781.00
664.00
722.00
615.00
669.00
908.00
1122.00
1000.00
842.00
708.00
421.00
680.00
729.00
2077.00
544.00
561.00
501.00
335.00
325.00
785.00
631.00
977.00
435.00
1532.00
748.00
1461.00
527.00
509.00
645.00
2046.00
674.00
863.00
845.00
319.00
814.00
438.00
1421.00
1069.00
1174.00
786.00
744.00
1123.00
1396.00
1059.00
434.00
760.00
383.00
920.00
460.00
1070.00
734.00
1245.00
664.00
528.00
1336.00
1129.00
X Occup led
Housing
With TV
IC2b
95. SO
97.40
97.20
97.00
97.10
96.50
97.00
95.60
96.70
97.60
96.10
97.00
97.20
97.60
95.90
97.70
95.00
97.30
97.50
96.40
96.40
96.70
95.30
96.90
94.40
94.70
96.60
95.80
96.40
95.90
95.00
96.40
95.30
94.90
97.30
96.80
96.10
95.90
95.20
97.00
95.90
96.40
96.00
97.90
97.00
95.70
97.30
94.70
97.70
95.50
97.50
95.60
95.60
96.30
93.90
95.10
95.00
93.00
95.60
94.60
96.70
97.30
95.80
97.40
96.30
97.40
Local Radio
Stations/
1,000 pop.
IC2c
.03
.88
1.10
.91
.21
1.36
.77
1.89
.72
1.33
.45
.93
.82
1.20
.96
l.OS
1.46
.52
.96
1.04
.31
2.22
.82
1.35
2.86
1.15
1.26
2.83
0.00
.87
.36
1.33
1.68
1.26
.99
1.15
2.21
1.62
.28
.16
1.61
2.02
1.29
0.00
.51
2.68
.66
2.08
1.20
2.31
1.47
1.37
.97
2.68
1.85
.43
1.10
.80
.93
1.89
l.SO
1.57
.88
1.15
.69
.93
Population
Density
In SMSA
IC3a
360.00
752.00
326.00
501.00
1816.00
804.00
917.00
272.00
2791.00
849.00
1877.00
644.00
1359.00
614.00
345.00
498.00
335.00
2152.00
509.00
476.00
675.00
380.00
274.00
988.00
1056.00
316.00
361.00
690.00
12963.00
4S3.00
1728.00
910.00
565.00
621.00
964.00
860.00
336.00
532.00
5415.00
2648.00
998.00
302.00
351.00
3190.00
1356.00
106.00
788.00
276.00
1340.00
433.00
381.00
233.00
574.00
526.00
441.00
42.00
319.00
1254.00
819.00
336.00
991.00
263.00
777.00
456.00
1216.00
524.00
X Pop.
under 5
and 65+
in Central
City
IC3b
18.30
20.00
21.90
20.60
14.50
17.90
19.00
19.40
20.60
21.30
19.00
21.50
19.50
17.60
17.10
19.00
19.60
20.30
24.60
18.70
16.80
20.90
15.70
20.30
15.00
15.90
17.70
16.10
19.60
19.90
18.20
20.10
17.40
20.80
19.70
22.40
16.60
19.10
19.80
18.70
15.20
19.20
19.00
21.40
19.80
17.40
20.20
21.80
22.30
18.90
23.10
19.10
22.70
22.10
18.10
16.40
16.60
20.00
16.00
19.70
21.00
21.30
20.00
19.90
17.30
20.20
Negro to
Total Pop.
Median
Family
Incoma Ad j .
For Education
IIA1
.76
.83
.78
.76
.68
.74
.83
.79
.63
.81
.75
,7B
.79
.81
.69
.86
.70
.81
.91
.73
.88
.77
.7*
.72
.55
.73
.84
.76
.76
.77
.70
.75
.79
.84
.78
.68
.77
.64
.75
.74
.85
.82
.68
.71
.78
.68
.76
.74
.64
.80
.77
.70
.76
.67
.71
.72
.75
.72
.87
.80
.70
.74
.86
.83
.74
.88
Negro to
Total Pop.
Professional
Emp. Adj.
For Education
IIA2
.07
.04
.02
.01
.01
.13
.15
.19
.02
.04
.09
.06
.09
,06
.07
.08
.02
.10
.09
.05
.13
.02
.13
.04
.01
.10
.07
.IS
.05
.07
.06
.07
.24
.09
.03
;01
.15
.15
.10
.09
.18
.06
.04
.03
.09
.02
.04
.01
.02
.14
.03
.03
.10
.01
.05
.03
.02
.05
.01
.02
.02
.02
.06
.06
.14
.04
Negro Kales
To Total
Males
Unemployment
Rate Adj
For Education
IIA3
2. Ox
3.07
3.0"
3.60
1.4H
2.01
2.21
2.03
1 .4V
2.63
?.3v
2. 68
2.5<
2. no
2. \i
2.3?
l.KK
2. It*
2.11
2.02
1.7?
3.30
2.1?
2.?«
1.1?
2.03
3.10
2.?<-
1 .30
2.6?
1 .75
P.23
2.63
1.73
3.11
?.13
1.H1
2.24
1.65
2.2?
2.1 1
2.4f>
?.
-------
TABLE A-5 (Continued)
Ob
1 AKR
2 ALB
3 ALL
4 ANA
b AIL
6 BAL
7 HlH
8 DOS
9 M/F
10 CHI
11 CIM
IP CLE
13 COL
14 UAL
16 DAY
lt> DEN
17 UEI
IB FOB
19 FOW
20 hAU
21 faHA
22 SHE
23 HAN
24 WON
2S «OU
26 I NO
27 JAC
2H JtH
29 KAN
30 LOS
31 LOU
3^ MEM
33 «1A
3» MIL
35 MlN
36 NAS
37 ME«
3U Nr «
3V HIM
40 NOR
41 OKL
»/• DMA
43 PAT
44 PHI
4b KHO
46 PIT
47 POM
4B PKO
49 MIC
40 HOC
51 SAC
« STL
b3 SAL
54 S«N
bb SAN
b6 SAN
b7 SAN
5H SAN
b9 SEA
60 SHW
61 SYW
h2 TAH
6J IOL
64 NAS
65 YOU
Negro Females
To Total
Females
Unemployment
Rate Ad].
For Education
IIA4
1.79
2.09
1.30
1.05
1.52
1.78
1.67
2.14
1.67
2.32
1.99
1.88
1.82
1.58
1.69
1.75
1.56
1.86
1.87
1.93
2.09
1.90
1.73
2.28
1.93
1.80
1.90
2.16
1.17
1.90
1.49
1.66
2.13
1.33
2.37
1.46
1.61
1.97
1.23
1.61
1.72
1.S7
2.45
1.41
1.66
1.29
1.77
1.45
1.37
2.06
2.15
1.5S
1.87
2.04
1.31
1.51
1.29
1.82
1.40
1.27
2.24
2.46
1.72
2.27
1.46
2.31
Male to
Female
Unemployment
Rate Ad]
For Education
IIB1
.75
.66
.94
.62
.66
.60
.70
.62
1.00
.82
.73
.63
.80
.86
.74
.65
.92
.83
.84
.66
.46
.77
.48
.90
.74
.59
.66
.64
.63
.74
.91
.59
.70
.74
.82
.90
.94
.79
.81
.63
.47
.71
.82
.58
.77
.80
.76
1.03
.76
.62
.70
.89
.78
.75
.70
.77
-.80
.92
.68
.92
.71
1.00
.67
.71
.76
.75
Male to
Female
Professional
Emp. Ad].
For Education
IIB2
1.49
.76
.67
.76
.16
.43
.62
.02
.56
.46
.63
.62
.67
.55
.73
.75
.67
.87
.44
.91
.37
.44
.27
.67
.28
.81
.57
.18
.38
.33
.77
.29
.17
.49
.61
.73
.31
.43
.53
.75
.06
.64
.24
.76
.70
.65
.75
.48
.48
.35
.85
.64
.47
.86
.19
.48
.69
.59
.18
.84
.22
.57
.28
.44
.74
.41
X Working
Outside
County of
Residence
I1C1
17.80
21.00
26.90
25.10
25.00
36.00
34.90
5.50
31.00
6.60
10.80
23.30
11.10
5.60
8.30
16.70
35.80
22.80
15.90
12.50
18.00
9.90
13.80
10.70
.90
4.90
16.90
2.60
33.00
28.60
2.90
12.60
4.20
3.50
14.60
23.10
8.60
26.60
42.20
31.40
27.20
8.50
16.30
38.20
28.40
1.50
11.80
24.40
24.70
44.00
8.30
10.80
34.20
11.70
3.00
16.20
1.50
24.10
12.20
10.70
15.20
9.80
7.10
18.10
43.50
19.00
Central
City i
Suburban
Income
Dlst.
IIC2
.06
.04
.04
.02
.06
.08
.12
.06
.08
.08
.10
.02
.16
.06
-.06
.08
-.02
.10
-.06
.02
.06
.02
-.06
.10
-.14
-.04
-.04
0.00
.04
.02
-.02
.06
-.06
.08
.08
.04
-.06
.04
.06
.16
.04
-.02
-.04
.08
.08
-.02
.02
0.00
0.00
.04
.08
-.02
.08
-.06
.08
-.02
-.06
.02
.10
-.04
.06
0.00
.04
.02
.04
.04
Housing
Segregation
Index
HC3
.27
1.20
1.32
.82
1.3T
1.29
.96
.43
2.56
1.51
.85
1.52
1.38
.60
.57
1.T6
1.24
1.42
.22
.84
.^0
1.62
.50
2.63
.44
.34
.46
.01
1.10
.84
.52
.95
.14
.52
.94
1.29
.12
.46
.29
1.89
.27
.58
.43
2.16
.92
.40
1.85
1.53
1.18
.68
1.61
1.22
1.55
.58
.12
.70
.66
.93
.46
1.28
.81
1.94
.64
.68
1.89
1.27
X Families
With Income
Above Poverty
Level
IIU1
89.30
93.90
93.90
94.80
94.80
90.90
91.50
84.50
93.90
93.20
93.20
91.90
93.10
92.40
91.40
94.00
93.20
93.50
92.10
92.00
93.00
93.90
89.70
95.10
92.80
90.20
93.50
85.90
90.90
91.10
91.80
91.40
83.20
89.10
94.30
95.40
88.80
83.60
90.80
93.20
86.60
90.60
93.20
95.70
92.70
91.10
92.80
93.10
92.20
91.10
94.80
91.40
91.90
92.50
84.00
89.70
91.40
92.80
94.40
94.80
93.30
92.90
B9.30
93.40
93.90
93. SO
X Occupied
Housing
With
Plumbing
niA2
94.50
97.10
96.60
95.80
99.50
97.50
97.70
91.30
97.30
98.30
97.60
95.70
98.50
97.50
97.80
97.20
97.70
98.30
98.40
98.40
97.00
97.90
93.60
98.00
97.10
97.30
96.50
95.50
95.10
97.40
98.80
96.60
94.50
97.30
97.40
97.10
94.30
97.00
97.90
97.90
97.10
98.10
97.00
98.60
98.40
97.90
96.00
97.40
97.50
95.90
97.30
99.10
96.40
98.60
94.30
98.80
98.50
97.30
99.20
97.80
97.80
96.90
96.90
97.00
98.50
96.60
X Occupied
Housing With
1.01 or More
Persons
Per Room
IIIA3
8.00
5.70
4.20
3.70
5.90
7.30
6.70
9.20
5.40
5.20
8.00
9.10
5.30
5.80
8.40
6.00
5.20
7.60
6.70
8.10
11.80
6.00
7. SO
5.60
19.10
9.90
7.70
8.10
9.40
5.90
8.20
8.70
12.30
13.30
6.50
6.40
7.30
13.10
8.60
6.30
7.70
6.40
7.70
5.00
5.20
9.60
5.90
4.20
6.10
5.90
4.50
6.90
9.80
9.30
14.90
8.80
7.20
5.90
6.40
4.10
6.20
5.00
5.60
6.00
6.70
6.70
X Occupied
Mousing With
Telephone
IIIA4
87.30
93.80
92.60
94.10
93.30
89.30
67.80
H6.90
92.50
93.00
«7.40
90.40
92.80
92.30
86.40
91.90
91.60
92.20
66.90
88.50
89.90
93.70
85.40
92.90
92.40
66.20
69.40
B2.90
80.90
91.10
89.10
HV.lO
85.90
65.40
93.60
9b.8o
86.90
86.90
85.80
69.30
84.00
89.60
93.20
93.60
90.70
63.60
94.40
91.90
91.70
87.20
91.90
91.10
90.00
92.90
85.80
87. ?0
91.60
91.20
94.00
91.70
91.90
92.60
62.70
92.80
92.70
93.00
242
-------
TABLE A-5 (Continued)
AKH
ALH
ALL
ANA
ATL
HAL
oIP
HOS
MUF
CHI
CIN
CLE
COL
OAL
f)AY
OEN
Dtt
FOW
FOR
GAR
BRA
GKE
HAR
HON
MUU
IND
JAC
JEW
IVAN
LOS
LOU
Mb M
MIA
MIL
M I N
NAS
NEW
Nt»
NEW
NOH
OKL
DMA
h*AT
PHI
PHO
f-lT
PUR
PRO
SAC
STL
SAL
i>AN
SAN
>AM
SAN
>AN
>tA
fAM
POL
.AS
rou
% Workers
Who Use
Public
To Work
IIIA5
6.90
2.10
7.60
3.30
.40
9.40
13.80
6.20
20.00
10.40
23.20
8.30
13.30
6.10
6.30
5.40
4.40
8.20
2.10
2.70
7.70
2.20
3. SO
9.90
7.40
5.40
5.80
6.70
35.60
5.50
5.60
6.70
9.90
9,10
12,00
9,10
6.60
20.40
48,00
18.50
9.70
1.60
7.20
14.40
20.60
1.30
14.50
6.00
5.30
13.00
8.00
2.30
5.10
2.30
5.80
90
4.30
15.40
2.30
7.10
5.00
6.80
3.10
3. BO
16.50
2.30
Total
100,000 pop.
IIIA6
2829.50
2709.20
1651.90
1590.10
39*0.20
4024.50
4051.70
2870.40
3404.00'
2676.60
2913.50
2643.80
2942.80
3251.00
3680.30
2361.10*
5014.20
4618.10
4334.30
2792.50
4206.00
2312.10
2818.90
2253.50*
3126.50
3569.60
2660.50
4321.20
3136.50
3419.40
5431.70
3043.10
4051.40
5151.40
2250.30
3497.60
3292.60
3934.00
5094.20
3703.10
3354.10
3086.40
3123.70
2523.70
2SB7.90
4652.00
1888.50
4358.90
3398.20 •
3584.60
2100.00
4415.60
3587.10
3440.80
3561.30
4388.30
3350.00
5005.90
3736.20
3872.00
3899.80 •
1675.90
3982.00
2910.50
34B0.30
2065.80
Coat of
Living
Index
IIIA7
100.00
105.60*
112.10
111.00
106.50*
97.10.
102.90*
102.40
120.80*
104.80
109.60
96.20
106.30*
105.30
95.30*
104.30*
108.30
106.50*
114.60
93.30
103.90*
94.80
105.30
122.20*
124.60*
95.00
99.10
102.80
124.10
109.90
106.30*
100.40
98.60*
112.10*
107.70*
119.00*
103.40*
100.30
115.60
131.50*
95.40
96.90*
97.30
127.20*
123.30*
111.70*
104.30*
88.10
112.90
102.00
116.70*
103.90*
101.00
99.10
100.90
103.10
100.40
124.30
113.00
118.30*
115.90*
112.60 •
102.10
102.70*
110.40*
101.40*
Public
Swimming
Poola/
100, 000 pop.
IIlBla
N.A.
a. so
15.20
1.80
14.70
41.70
28.90
32.40
5.40
10.30
25.30
19.40
33.40
9. BO
86.10
17.60
34.20
8.50
19.30
32.80
22.10
13.00
5.00
16.60
1.00
7.60
17.10
15.10
9.90
13.60
14.40
20.60
19.50
23.70
17.10
9.40
46.20
1.00
10.00
17.20
2.90
99.80
38.90
9.60
14.10
38.20
5.40
16.80
9.90
11.60
18.10
38.70
10.20
12.50
24.30
35.00
25.00
17.70
29.10
7.70
71.70
40.90
5.90
37.50
28.70
18.70
Public
Camping
Site./
100, 000 pop.
IIIBlb
N.A.
0.00
542.00
121.30
1102.10
0.00
182.50
200.20
1*3.00
252.70
132.90
237.50
202.50
478.10
496.10
1023.50
666.10
292.30
72.50
212.50
252.70
1044.50
132.40
70.80
0.00
222.20
90.10
113.40
0.00
177. BO
271. BO
1293.60
39.00
195.60
194.40
343.40
1730.10
0.00
150.40
8.10
0.00
29.60
533.30
1.50
47.30
665.30
113.30
1388.50
1191.00
139.00
997.70
997.50
157.00
795.70
170.10
3628.20
11001.50
191.00
272.30
846.00
160.40
930.80
536.00
277.10
127.20
910.40
Public
Tennla
Coutta/
100,000 pop.
IIIBlc
N.A.
101.60
99.90
1.5.46
111.60
189.20
90.70
136.60
63.90
252.70
188.60
33. »0
130.80
29.40
152.30
172.90
151.40
112.10
196.70
108.90
175.30
254.20
43.00
79.80
4.90
14.10
86.50
64.30
4.90
69.40
44.90
183.80
77.90
123.80
145.30
251.40
166.40
17.20
33.10
130.90
208.50
140.40
164.80
77.30
89.90
74.40
30.80
126.90
108.70
1*2.90
103.10
88.60
42.30
86.00
60.20
72.60
188.50
128.60
105.20
139.90
113.20
117.90
73.00
138.50
168.50
69.00
Hilea of
Trails/
1,000
pop.
IIIBld
N.A.
148.70
22.10
165.40
51.40
56.80
11.50
18.90
89.60
42.20
34.10
119.80
112.40
51.30
70.60
123.50
205.20
51.10
9.60
68.20
47.30
2*3.00
19.90
63.30
9.50
27.70
93.70
5.70
3.30
56.60
156.30
72.60
80.50
128.50
74.10
236.50
175.60
3.80
30.00
80.20
13.20
352.60
88.90
14.70
112.30
263.40
41.20
659.10
199.80
54.10
231.00
1968.80
37.70
363.80
67.10
955.40
134.80
206.10
200.00
643.50
701.90
51.90
28.60
14.40
197.10
33.60
Bank a and
S&L
Aaaoc./
1,000 pop.
III82
.09
.04
.05
.09
.03
.06
.05
.04
.07
.02
.08
.15
.03
.04
.09
.06
.09
.02
.07
.06
.06
.04
.05
.06
.03
.08
.06
.07
.05
.12
.02
.04
.02
.07
.10
.07
.05
.06
.02
.06
.03
.10
.08
.06
.06
.01
.06
.04
.04
.04
.0*
.03
.09
.05
.05
.03
.02
.02
.02
.04
.07
.04
.08
.04
.04
.04
Retail
Trade
Establishments/
1,000 pop.
II1B3
8.68
6.52
8.65
8.92
7.11
6.73
6.99
7.45
7.71
8.40
6.97
7.39
7.01
6.66
6.49
6.41
7.66
6.3?
8.02
8.20
6.61
6.96
R.39
7.12
6.10
8.17
6.80
8.12
10.04
7.79
8.15
7.19
6.78
8.14
7.98
6.08
7.73
7.44
8.77
8.44
5.55
9.11
7.14
6.54
8.18
7.52
B.07
7.57
8.71
6.33
7.2?
7.83
7.67
6.57
7.91
8.11
6.78
8.14
6.42
7.24
8.10
8.50
8.42
7.25
4.62
7.58
Selected
Service
Establishments
1,000 pop.
IIIB4
5.B5
4.94
5.20
5.61
5.39
5.31
4.4?
4.42
6.08
5.44
5.41
5.06
5.31
s.n/
6.b?
5. 1ft
6.5<>
4.6o
7.30
6.24
4.2S
4.>V
5.(i«
4 .98
5.10
6.11
5.47
5.90
5.04
6.47
7.53
5.37
4.51
7.«,4
4.77
4.7?
6.?2
5.5H
«i.7h
S.»>4
4.11
7.4.1
6.2i
5.61
5.21
5.97
5.50
5.8V
5.7?
4.70
5.10
5.9S
5.74
5.6%
4.90
5.95
b.26
7.0ft
5.39
5. 44
5.93
5.76
7.05
5.52
4.53
5.41
243
-------
TABLE A-5 (Concluded)
us
1 AKR
I >LH
3 AIL
4 ANA
5 AIL
6 HAL
7 hlH
8 BOS
1 HUF
10 CHI
11 UN
12 CLE
13 COL
14 DAL
15 DAY
16 DEN
17 DtT
1H FOR
19 FOR
20 GAH
21 GriA
22 GKE
23 HAK
?4 HON
25 NOU
26 1NO
27 JAC
AN
57 SAN
58 SAN
59 SEA
60 SPR
61 SYR
6? 1A»
63 TOL
64 HAS
65 YOU
Hospital
Beds/
100,000
pop.
IIIB5
414,90
329.60
447.60
363.90
258.80
321.00
377.70
493.30
466.60*
493.80
436.90
370.70
418.30
352.00
346.50
332.40
448.00
339.50
341.90
365.20
336.10
309.50
415.10
383.50 •
230.60
431.20
412.70
365.70
3V3.30
470.00
368.50
418. f)0
472.00
458.90
444.40
547.20
539.60
507.20
462.50
436.90
281.40
405.80
694.60
336. bO
429.80
351.20
494.00
415.20
408.00 •
506.30
290.60
414.30
496.40
334.10
332.30
364.40
266. 7-0
444.30
312.30
318.70
437.50*
291.40
394.50
464.80
306.90
366.80
Vols. of
Books In
Main
Public
Library/
1 ,000 pop.
IIIB6
1568.40
1040.50
316.10
253.70
155.40
480.10
913.90
1070.30
957.70
1836.90
599.60
1888.00
15S8.50
1025.40
672.90
1344.20
953.90
503. BO
273.00
815.40
632.40
1027.50
455.30
691.20
1777.90
580.70
788.00
1133.10
063.40
922.30
437.90
1018.20
1097.60
572.30
14S2.60
612.60
620.50
603.10
622.70
552.00
560.00
873.30
776.90
111.10
526.20
678.60
863.90
970.80
644.00
819.20
BS3.00
964.10
560.90
748.60
753.20
501.30
716.60
405.70
653.10
935.60
1082.50
557.00
369.70
1938.30
677.90
1096.70
Death
Rate/
1 , 000 pop .
9.50
8.50
10.90
10.40
6.00
7.80
9.40
10.30
8.10*
10.10
9.70
10.00
9.60
8.20
7.70
7.80
7.40
8.60
11.10
7.90
8.20
8.20
8.40
8.50.
4.80
7.00
9.10
9.00
12.20
9.20
9.00
9.50
9.10
10.50
8.90
7.70
9.10
9.70
10.50
9.80
7.70
8.00
8.30
9.00
10.10
7.90
10.70
9.90
10.50.
9.80
9.00
7.80
10.00
6.20
7.50
8.90
7.50
9.10
5.70
8.50
10.00 «
9.30
13.70
9.80
7.00
9.90
Birth
Bate/
1 000 DOD
* ,vw PUP*
HIC2
17. SO
17.40
16.80
14.60
18.20
19.90
17.10
17.10
16.70*
16.80
18.30
18.30
17.20
19.40
19.70
18.30
17.50
18.20
13.60
18.90
1%.30
18.20
17.70
1ft. 60.
20.00
19.10
18.40
19.10
17.10
17.40
16.10
17.90
19.50
14.40
17.80
19.00
16.70
19.30
16.40
16.30
19.60
17.80
19.00
14.90
16.90
18.10
14.80
16.10
20.80 •
17.50
18.80
16.30
17.60
22.70
21.30
17.50
17.10
16.10
18.50
17.80
15.80*
18.70
13.70
17.80
19.50
16.20
Sports
IIIC3
K.A.
9.00
7.00
7.00
6.00
17.00
26.00
5.00
20.00
22.00
22.00*
13.00
26.00 •
7.00
15.00
6.00
15.00
12.00*
5.00
5.00*
12.00*
11.00
6.00
15.00
4.00
15.00
11.00
6.00
4.00
15.00
22.00
11.00
4.00*
16.00
17.00*
14.00
9.00*
12.00
22.00*
14.00*
13.00
10.00
9.00
5.00
17.00
14.00
17.00
12.00*
6.00
6.00 •
8.00
4.00
19.00
7.00*
15.00
0.00
22.00*
12.00
9.00
9.00
7.00
12.00
12.00
9.00
3.00
S.OO
Dance
Drama
and
Mu.lc
IIICAa
N.A.
61.00
51.00
3V. 00
28.00
66.00
71.00
40.00
67.00
66.00
84.00*
39.00
71.00 •
6.00
31.00
49.00
81.00
3S.OO*
35.00
40.00*
52.00*
29.00
68.00
81.00
12.00
81.00
76.00
48.00
10.00
61.00
84.00
74.00
33.00*
59.00
66.00*
70.00
59.00 •
74.00
H4.00*
70.00*
S7.00
63.00
•39.00
la. 00
23.00
84.00
B4.00
54.00 •
19.00
48.00*
30.00
33.00
55.00
39.00*
24.00
14.00
66.00*
35.00
20.00
52.00
6.00
52.00
54.00
50.00
27.00
31.00
Cultural
IIIC4b
N.A,
7.00
3.00
2.00
0.00
10.00
11.00
4.00
4.00
13.00
7.00 •
4.00
11.00 •
4.00
4.00
8.00
15.00
23.00 •
1.00
4.00*
15.00 •
2.00
3.00
6.00
13.00
7.00
5.00
4.00
0.00
15.00
7.00
3.00
2.00*
9.00
10.00*
9.00
7.00*
11.00
7.00 •
9.00*
15.00
9.00
7.00
4.00
0.00
6.00
34.00
4.00*
7.00
4.00 •
5.00
2.00
H.OO
2.00*
10.00
1.00
13.00*
23.00
3.00
11.00
8.00
15.00
4.00
3.00
15.00
4. to
Fairs
and
Festivals
Held
IIlCAc
R.A.
15.00
2.00
7.00
4.00
9.00
14.00
12.00
6.00
7.00
21.00.
5.00
14.00*
7.00
2.00
6.00
12.00
0.00*
0.00
12.00*
9.00*
4.00
6.00
9.00
8.00
4.00
14.00
2.00
0.00
15.00
21.00 •
13.00
5.00 •
17.00
9.00*
8.00
19.00*
5.00
21.00*
8.00*
7.00
8.00
19.00
2.00
2.00
11.00
21.00
e.oo •
5.00
2.00*
6.00
5.00
22.00
7.00*
16.00
S.OO
7.00*
0.00
2.00
4.00
13.00
9.00
e.oo
7.00
IS. 00
14.00
244
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BASIC STATISTICS OF ECONOMIC COMPONENT (M)
us
66 ALM
67 ANN
6« APR
69 AUt*
(0 Alls
M HAH
If HAT
73 Hf A
74 H1N
7s H*i
7f) CAN
77 CMA
7« CHfl
/9 CHA
HO CHA
81 COL
Pf COL
H3 COL
H4 C0«
MS HAV
H6 [ItS
87 IHIL
HH t L^
«9 EKI
00 tUG
Si t VA
9," f AY
93 HI
94 F OH
9S f HE
9b C.HE
97 HAM
9K HAH
99 -lUN
100 ML*
101 JAC
102 JOH
103 3 "AL
!?<• xto
125 HOC
126 SAC,
127 SAL
128 SAN
129 SAN
13(. sCk
131 Sf-w
132 SOU
133 SMO
134 STA
13S STCi
136 TAC
137 THE
130 TUC
139 TUL
140 UTI
1*1 VAL
142 .AT
143 *ES
14* »1C
14b .IL
146 «IL
147 llOfi
14H YOW
Personal
Income
Per Capita
IA
3139.00
2872.00
3767.00
3004.00
2573.00
3014.00
2823.00
2H54.00
2897.00
3036.00
3748.00
3167.00
2440.00
2029.00
3207.00
2811.00
2920.00
2628.00
2496.00
8141.00
3290.00
3446.00
2735.00
2359.00
2829.00
3045.00
2832.00
2340.00
3261.00
3355.00
2761.00
2706.00
3111.00
3254.00
2584.00
2961.00
2548.00
2540.00
3382.00
2719.00
3097.00
3371.00
1546.00
3397.00
2764.00
3170.00
1011.00
?733.00
3453.00
2401.00
2565.00
3656.00
32H6.00
3102.00
301H.OO
3252.00
2S67.00
3370.00
3007.00
3250.00
3429.00
3152.00
3140.00
3369.00
3177.00
2735.00
2552.00
3194.00
3018.00
6360.00
3061 .00
3178.00
3631.00
29R8.00
3234.00
2928.00
3153.00
3486.00
3893.00
3159.00
2674.00
3426.00
3276.00
3259.00
Savings
Per Capita
IB1
702.00
625.00
517.00
562.00
640.00
745.00
435.00
639.00
726.00
80.00
114.00
1351.00
532.00
246.00
430.00
596.00
224.00
8*9.00
400.00
579.00
747.00
1733.00
562.00
323.00
584.00
83.00
1138.00
347.00
128.00
555.00
883.00
6S9.00
1068.00
822.00
650.00
309.00
715.00
340.00
H85.00
5H9.00
224.00
496.00
933.00
99.00
756.00
486.00
365.00
736.00
924.00
456.00
253.00
7?6.00
320.00
1K7.00
1006.00
363.00
336.00
1446.00
595.00
338.00
693.00
1033.00
470.00
1680.00
311.00
212.00
310.00
6H5.00
768.00
401.00
8925.00
14H7.00
576.00
763.00
674.00
290.00
265.00
737.00
1527.00
644.00
528.00
218.00
875.00
304.00
Property
Income/
Personal
Income
IB2
.14
.14
.14
.14
.10
.15
.10
.09
.12
.12
.22
.13
.09
.12
.11
.11
.12
.09
.11
.14
.16
.11
.13
.10
.12
.13
.14
.06
.10
.14
.12
.11
.13
.04
.11
.10
.12
.10
.IS
.12
.13
.11
.09
.15
.12
.11
.15
.12
.13
.14
.14
.16
.13
.08
.11
.12
.07
.13
.13
.11
.13
.13
.15
.21
.18
.08
.12
.14
.16
.22
.12
.11
.15
.15
.15
.13
.11
.16
.24
.13
.08
.16
.13
.12
7. Owner-
Occupied
Housing
Units
IB3
62.90
65.30
57.10
75.00
64.50
54.90
59.50
66.40
69.60
69.30
63.00
73.50
60.10
64.50
61.60
65.90
58.80
67.30
52.70
64.40
69.80
69.60
73.30
58.70
71.60
64.10
69.70
55.20
77.70
73.20
60.10
68.10
69.40
68.30
68.00
68.00
66.40
71.10
71.50
68.20
68.90
69.80
58.00
56.60
64.60
73.20
64.20
58.40
56.40
68.30
61.50
56.90
62.10
60.30
69.70
65.70
70.80
71.60
SB. 80
72.00
67.20
77.80
52.50
53.80
64.40
63.40
64.80
77.30
69.20
61.70
61.40
66.00
65.30
65.40
68.30
66.30
60.50
61.80
67.60
64.70
66.60
68.30
59.90
72.60
% Households
With One Or
More
Automobiles
164
82.50
89.70
91.20
89.70
82.30
90.40
89.20
88.10
88.10
85.40
85.20
88.00
HO. 10
80.30
84.90
82.70
92.80
85.20
82.10
H8.50
87.80
86.90
81.40
84.20
86.10
91.10
84.40
87.40
91.00
88.90
88.20
85.80
68.70
85.00
79.00
89.40
83.80
82.20
90. VO
84.90
84.70
91.40
92.00
81.10
84.60
91.20
82,50
HI, 90
85,90
83.10
79.70
81.90
89.00
H5.50
8H.40
93.80
86.40
88.50
87.10
82.80
88.90
89.30
90.40
90.40
89.40
78.90
81.90
87.00
85.30
89.50
85.80
88.40
82.60
90.00
88.40
83.00
90.70
84.40
87.50
90.80
79.80
87.20
82.30
87.90
Owner Occupied
Single Family
Houa Ing
(In $1,000)
IBS
17.10
15.70
23.30
17.00
14.00
16.60
14.40
17.90
11.50
17.70
2H.60
16.10
IS. 20
15.40
17.10
12.70
18.50
17.80
15.10
11.50
10.00
16.20
12.80
13.60
14.60
16.50
12.60
16.50
16.40
15.50
15.40
14.50
17.20
15.30
14.00
17.40
13.80
10.00
16.70
12. HO'
16.20
17.80
23. IP
19.50
14.50
18.30
19.40
14.20
21.70
13.10
16.40
24.80
20.20
18.00
15.50
23.20
12.40
15.9(1
18.80
12.80
18.70
16.40
23.00
23.40
21.10
12.30
13.60
12.70
14.10
50.20
16.75
17.50
17.40
16.70
13.40
16.10
19.40
20.70
17.70
13.50
11.10
17.10
18.80
14.90
Percent of
Families With
Income Above
Poverty Level
I1A
89.30
87.00
94.90
94.50
H4.60
89.20
87.40
86.40
H8.40
92.70
94.80
94.20
79.40
R7.00
90. 10
86.70
90.00
85.70
R1.30
hi. 60
93.30
93.90
91.70
82.60
93.20
92.10
90.90
82.90
93.10
94.90
R5.80
88.20
93.00
93.40
M5.20
86.50
81.40
90.30
94.20
B5.70
93.50
93.90
93.00
94.20
H6.60
94.30
93.90
84.90
84.60
Ml. 40
80.80
92.70
91.70
H9.90
HO. 70
92.60
84.50
94.30
H8.80
95.00
93.60
92.30
90.40
92.40
89.60
92.20
81.80
94.10
91.40
96.00
88.80
92.00
93.60
89.20
90.20
92.60
91.20
94.50
89.80
92.00
91.10
92.90
94.60
94.10
Degree of
Economic
Concentration
IIB
.00
.25
.20
.03
.20
.14
.46
.06
.14
.06
.OS
.OH
.02
.16
.02
.20
.24
.10
.16
.25
.01
.14
.32
.17
.07
.06
.07
.09
.06
.02
.29
.21
.09
.20
.00
.06
.10
.10
.02
.OS
.11
.16
.16
.01
.07
.07
.03
.07
.36
.05
.10
.08
.06
.05
.16
.22
.06
.03
.18
.10
.14
.02
.29
.16
• ?8
.01
.03
.03
.24
.26
.23
.19
.09
.35
.06
.04
.14
.00
.01
.OS
.01
.13
.08
.04
246
-------
TABLE B-l (Concluded)
us
66 ALB
67 ANN
68 APP
69 AUG
70 AUb
71 HAK
72 SAT
73 BEA
7* HlN
75 BHI
76 CAN
77 CHA
78 CfA
79 ChA
80 OA
81 COL
82 COL
B3 COL
84 COft
85 DAV
86 DES
87 DUL
80 ELH
89 fMI
90 F.UG
91 EVA
92 f ftY
93 FLI
94 FOK
95 FfiE
96 CiKF
97 HAM
98 HAW
99 HU**
100 MU'J
101 JAC
102 JOH
103 KA.
104 KND
105 LAM
106 LAY
107 LAS
108 LA«
109 LIT
110 LO-i
111 LO*
112 MA:
113 MA)
114 MCH
115 HOI
1)6 M »
117 NE *
118 Nt<
119 ORL
120 OX"i
121 PEN
122 PEI)
123 MAI
124 Ht.l
12S ROr
126 SA!>
127 SAl
128 SAN
129 SAN
130 SCH
131 SH.<
132 SOU
133 SPO
134 ST«
135 STO
136 TAC
137 THI
138 TU(
139 TUL
40 UT1
41 Vfcl
42 »A1
43 HE;
»4 «I(
45 »H
46 WU
47 HO*
48 TO*
Value Added/
Worker in
Manufacturing
(In $1,000)
1IC1
13. SO
17.10
16.20
13.20
14.90
9.40
16.00
28.30
35.10
11.60
14. SO
14.30
11.90
26.60
10.50
11. 60
12.00
9.50
10.70
24.20
IS. 60
15.40
11.50
10.40
13.60
11.40
15.50
8.40
15.20*
14.40
14.60
9.60
14.80
12.80
15.50
12.20
12.50
10.60
16.90
13.30
13.30
20.10
20.30
10.90
11.00
15.80
12.70
11.40
12.00
14.90
9.80
13.10
11. 60
9.40
12.50
14.90
17.30
16.40
10.40
10.60
14.40
17.50
18.60
12.80
12.90
8.70
12.70
24.60
16.00
19.00
19.60
14.30
13.70
11.30
12.30
14.60
16.70
11.80
15.30
12.70
9.30
12.40
11.80
11.60
Value of
Construction/
(in 51,000)
IIC2
4.30
8.01
11.84
3.61 •
6.05
7.10
5.30
3.97
1.16
2.06
3.53
6.45
2.40
l.lft
6.84
4.99
9.44
3.78
7.65
1.50
2.70
5.67
2.12
6.94
3.03
6.30
2.39
2.88
7.77
4.29
7.28
4.28
5.31
3.20
.94
2.41
3.94
1.03
6.70
3.59
2.11
5.24
11.52
3.61
5.56
6.95
S.OH
4.84
5.26
2.15
2.89
3.46
fc.39
6.88
6.86
9.73
5.25
5.20
7.01
2.56
3.58
7.02
7.83
7.29
7.19 .
1.14
4.20
3.36
7.62
3.60
5.62
6.01
6.25
10.08
5.40
4.85
8.92
6.29
11.65
2.23
1.87
3.49
S.47
3.04
Sales/
Employee tn
(in $1,000)
IIC3
33.00
30.20
34.20
28.40
33.60
29.10
34.50
33.40
33.40
35.60
33.00
32.00
31,50
33.10
32.70
31.10
33.60
31.70
30.80
30.50
32.70
28.30
31.80
28.70
31.10
34.20
29.30
32.70
34.40
29.20
36.10
34.10
32.20
31.20
31.80
32.00
33. BO
31.80
32.30
31.20
32.60
34.20
36.60
29.00
32.50
33.90
30.40
31.50
28.40
29.90
32.90
32.50
33.20
30.50
31.70
35.00
32.40
34.00
30.20
31.20
31.00
32.30
34.50
32.40
39.70
35.60
32.60
32.00
32.80
38.40
35.40
34.80
33.60
30.00
32.60
37.20
35.40
36.40
30.10
29.50
34.40
33.30
31.50
32.60
Stiff/
Enployee
in Wholesale
(In $1,000)
1IC4
130.60
80.40
89.40
102.20
76.90
77.70
114.60
97.00
101.80
79.90
89.30
85.50
89.70
76.80
185.20
114.80
62.60
97.60
84.30
88. SO
143.00
145.80
131.10
94.90
76.40
91.40
88.20
63. SO
61.50
101.50
100.30
101.20
79.70
98.00
102.30
77.40
95.30
75.30
74.90
87.60
93.70
118.70
105.10
85.60
94.10
68.00
B4.90
72.50
79.30
81.70
83.20
112.90
72.30
58.80
74.20
78.40
73.40
150.50
127.60
87.20
90.00
98.20
73.50
63. SO
78.00
71.60
88.50
89.70
98.50
115.90
112.00
109.70
97.30
78.10
120.70
87.10
77.60
104.70
74.00
113.60
84.50
142.30
86.50
89.90
Sales/
Employee
in Selected
(in $1,000)
I1C5
15.80
14.80
14.30
14.60«
10.50
11.20
14.10
12.00
12.20
12.30
12.50
13.30
11.70
12.00
13.80
11.10
13.10
10.50
10.20
11.50
12.40
13.30
13.90
11.00
13.60
13.10
13.90
10.90
13.10
14.60
13.70
16.90
13.00
11.90
10.70
16.30
12.30
14.70
13.60
11.40
14.30
13.60
15.80
14.50
11.30
11.80
12.40
11.30
12.40
10.80
11.90
11.20
14.00
11.40
11.80
13.50
12.20
13.40
16.30
14.20
14.30
13.60
14.20
16.00
15.80
14.80
11.90
13.40
13.20
14.90
14.50
14.90
13.20
11.70
14.30
14.70
14.90
11.40
10.70
12.90
15.70
14.10
14.30
13.70
Total Bank
Per Capita
IID
2492.00
1795.00
1756.00
1844.00
1286.00
2281.00
1502.00
2550.00
1594.00
2250.00
3542.00
1671.00
765.00
2186.00
2605.00
IBB?. 00
1268.00
1077.00
1020.00
1382.00
2120.00
2848.00
2010.00
1287.00
1825.00
1220.00
1997.00
576.00
1928.00
2370.00
1624.00
1088.00
1112.00
1906.00
1552.00
880.00
2538.00
1721.00
1935.00
1625.00
2004.00
1989.00
1898.00
2974.00
1867.00
1672.00
1794.00
1236.00
1766.00
1372.00
1888.00
3472.00
2320.00
1031.00
1754.00
1058.00
877.00
1818.00
2122.00
2540.00
1923.00
1690.00
1628.00
1744.00
2147.00
2584.00
2072.00
2172.00
2257.00
4188.00
20024.00
1512.00
3136.00
1557.00
24S2.00
2554.00
1459.00
2974.00
2125.00
2056.00
2193.00
2596.00
3345.00
2265.00
Central City
sad Suburban
Distribution
Iltl
.06
-.12
-.08
-.04
.04
.00
-.06
-.08
.00
-.02
.10
.04
-.06
-.12
-.02
.OR
-.04
.00
-.10
-.06
-.04
.00
-.06
-.04
.02
-.06
-.02
-.Oft
.00
.04
-.04
-.04
-.02
.04
-.10
-.18
-.14
.00
.04
.00
.04
.00
-.02
.04
-.14
.02
.04
.00
-.02
-.12
-.12
.10
.00
.06
.00
-.02
-.04
-.04
-.10
.06
-.04
.04
-.06
-.06
-.08
-.02
-.12
-.04
-.02
.26
-.02
.00
.18
.09
-.1?
.02
-.08
.06
.04
-.06
.00
.04
.00
.04
Percent of
Families With
Income Below
Pov . Level of
$15,000
IIE2
31.30
32.90
39.90
25.10
29.40
31.20
30.10
34.50
27.60
27.30
35.60
24.80
34.20
28.30
30.90
27.90
26.40
30.50
30.00
33.00
28.30
29.60
20.90
31.60
22.40
24.60
24.40
26.40
33.40
28.60
31.50
2S.40
28.10
26.20
26.30
3ft. 00
34.70
20.90
31.40
21.40
24.60
33.10
33.20
28.60
27.60
27.40
29.20
31.60
32.60
30.60
34.30
24.80
30.90
29.50
29.50
34.40
27.70
28.00
31.90
22.40
29.60
31.70
30.50
33.90
29.90
20.00
22.20
25.80
26.60
56.30
30.20
30.00
33.80
29.00
27.90
22.60
30.10
32.00
32.20
25.70
29.80
31.50
28.60
33.10
Un«
Rate
1IF
4.40
5.50
5.00
3.30
3.90
3.10
6.70
4.50
4.40
3.70
3.80
4.30
4.10
4.10
2.70
3.00
5.50
3.00
4.70
4.30
4.50
2.80
7.30
S.20
4.10
8.10
4.40
5.20
b.30
3.10
0.00
2.60
3.70
2.20
5.10
4.40
3.40
4.90
4.70
3.90
2.10
5.10
5.20
4.10
3.30
3.70
4.10
3.90
2.90
5.30
3.80
3.40
3.90
3.60
4.80
5.90
5.00
3.20
2. SO
2.40
4.00
4.90
7.00
6.40
7.30
5.20
5.00
4.70
6.90
2.30
8.20
8.40
3.50
4.00
4.60
5.70
6.70
4.80
3.00
7.10
4.00
3.80
3.60
2.30
Chamber of
Connie rce
Employees/
100,000
Pop.
IIG
NA
. 60 a
3. HO
2.50
2.40
1.8»
.60 •
4.60
3.20
5.JU
2.60
3.50
7.20
3.00
.SO •
6.90
4.70
6.20
b.40
4.60
.60 •
7.70
5.30
5. HO
2.70
3.80
5.20
.9u a
l.?0
4.30
2.90
7.3"
1.80
2.40
1.6U
l.HO
4.611
2.30
«.SO
b.OO
2. SO
1.90
3.30
2.60
.60 •
.«0
.90 •
1.00*
4.10
.50*
1.00*
7. 00
1.00«
1.70
.SO •
1.10
2.90
3.20
3.10
1. 70
.70*
4.50
2.00
.80*
1.00*
6.40
6.80
7.50
6.60
4.90
2.40
2.20
2.60
.60*
8.00
.60*
2.00
8.60
.60*
4.60
2.90
1.40
7.30
2.40
247
-------
TABLE B-2
BASIC STATISTICS OF POLITICAL COMPONENT (M)
IIS
066
067
06H
069
070
071
072
073
07*
075
076
077
07H
079
OHO
OH1
088
<1.l>0
713.00
811.00
769.00
563.00
604.00
856.00
1611.00
453.00
2497.00
762.00
3H5.00
722.00
1377.00
723.00
936.00
?043.00
611.00
802.00
712.00
1705.00
501.00
454.00
604.00
642.00
501.00
595.00
940.00
1228.00
584. 00
16S5.00
311.00
1075.00
898.00
126K.OO
1 04H.OO
567.00
664.00
372.00
639.00
1069.00
944.00
648.00
1014.00
752.00
292.00
464.00
663.00
1046.00
333.00
541.00
634.00
412.00
539.00
1294.00
643.00
947.00
1156.00
610.00
901.00
1 occupied
housing
vlth TV
IA2
95.50
95.00
95.00
96.50
94.60
94.30
94.9(1
96. 51)
95.90
96.60
97.50
97.10
94. «0
95.40
95.90
96.10
95.50
95.40
95.30
94 .30
97.00
96.70
96.00
95.60
97. Ml
93.70
97. SO
05.90
97.50
96. HO
95.00
95.90
97. 20
96.50
95. 90
96.60
95.00
96.70
97.30
95. bo
H9..10
96.50
96.10
97.30
95.90
97.70
97.90
95.50
95.00
94.20
95.20
96.70
96.40
95. 90
1-5.60
96.60
94.70
96.00
95.80
96.50
96.50
97.90
93. 80
93.30
93.70
9K.10
95.20
97.30
95.50
96.00
94.00
95.40
97.no
9».60
95.70
97.20
95. BO
97.60
95.10
95.80
97.60
97.40
97.81)
96.00
Local radio
Htatlons/
1,000 pop.
I A3
.03
4.41
1 ,2H
.!<
).95
3.71
3.34
3.15
1.58
1.32
1.54
.80
1.97
f.47
2.20
2.95
5.93
2.47
3.34
2.10
1.37
2.44
3.01
2.7H
1.13
4.69
1.71
1.41
1.20
2.50
2.90
^.33
1.32
1.4b
1.96
2.63
1.93
1 .52
2.97
2.75
1.56
2.11
3.29
.86
2.16
.36
1.87
2.42
2.41
2.65
4.47
1.12
.4K
.34
^.33
.53
3.29
1.16
3.07
1 .6H
1.83
1.81
1.60
3.40
1.46
2.13
3.05
1.7H
5.57
.48
2.41
2.16
.98
1.69
1.67
2.05
0.00
1.41
.85
2.82
1.75
1.20
2.03
1.21
Pres. vote
cast/voting
age i>op.
IB
54.90
71.60
65.90
63.40
41.10
67.20
57.80
SI. 70
51.70
64.30
69.70*
64. SO
44.90
75.80
45.70
54.50
5?. 30
42.10
29.70
SI .50
65.70
77.10
71.30
39.00
62.40
70.00
7T.90
25.70
63.10
85.30
56.70
36.70
50.70
53.10
66.10
SS.70
46.10
5H.60
64.50
50.00
51.00
67.50
51.40
64.10 •
4«.30
57.60
64.10 •
46.50
71.40
44.60
52.80
68. 30*
61.70*
52.70
59.40
66.20
54.80
62.60
51.60
51.70
60.50
59.80
48.60
68.90
76.60
58.40
SI. 60
66.80
65.80
69.70*
54.10
54.00
63.60
56.90
71.20
63.70
59.10
6B.30*
61.20
ST. 40
58.40
66.60
64.10«
51.80
Avg.
nonth ly
earnings
of teachers
HA1
6K2.00
624.00
759.00
658.00*
SS4.00
558.00
751.00
702.00
588.00
715.00
783.00
627.00
540.00
609.00
638.00
626.00
597.00
524.00
603.00
562.00
660.00
695.00
656.00
651.00
635.00
672.00
687.00
623.00
777.00
788.00
765.00
512.00
669.00
602.00
56V. 00
480.00
S20.00
624.00
679.00
567.00
61ft. 00
778.00
756.00
6S6.00
s«».oo
625.00
622.00
647.00
598.00
603.00
629.00
702.00
684.00
606.00
646.00
804.00
675.00
690.00
634.00
515.00
708.00
739.00
792.00
878.00
880.00*
5HO.OO
568.00
672.00
722.00
633.00
862.00
706.00
841.00
871.00
556.00
784.00
777.00
715.00
712.00
604.00
623.00
731.00
671.00
627.00
Av,.
nonthly
earnings
of other
enployees
11*2
SIS. 00
454.00
543.00
497.00 •
329.00
436.00
592.00
427.00
412.00
444.00
550.00
458.00
318.00
356.00
430.00
396.00
443.00
391.00
338.00
397.00
453.00
495.00
465.00
356.00
419.00
523.00
403.00
375.00
•.66.00
461.00
582.00
358.00
461.00
3H2.00
362.00
379.00
335.00
355.00
S18.00
381.00
400.00
506.00
582.00
492.00
354.00
499.00
504.00
341.00
sjt.oe
381.00
344.00
516.00
506.00
332.00
389.00
596.00
359.00
470.00
369.00
417.00
496.00
466.00
582.00
581.00
650.00*
387.00
377.00
400.00
S36.00
558.00
610.00
563.00
519.00
487.00
438.00
411.00
576.00
491.00
412.00
459.00
387.00
442.00
526.00
375.00
salary of
patrolmen
IIA3
6848.00
6096.00
6900.00*
7254.00
6900.00*
6447.00
8826.00
6000.00
6600.00
6940.00
7676.00
7950. CO
5676.00
6402.00
7052.00
6900.00*
6612.00
5460.00
5545.00
6270.00
6672.00
7507.00
7869.00
6900.00*
7011.00
7122.00
6900.00*
5364.1)0
6783.00
6900.00*
9222.00
5432.00
6424.00
6200.00
5H44.00
5999.00
6546.00
6900.00 •
7911.00
4800.00
6413.00
8064.00
6358.00
7345.00
6456.00
7618.00
7010.00
5352.00
7471.00
5364.00
5520.00
8696.00
tBjb.OO
5X80.00
7260.00
H3'.0.00
6214.00
8312.00
64)2.00
6350.00
7560.00
8042.00
7854. ,00
6772.00
7UO..OO
63S3..00
50*0,00
6500.00
6900.00*
7525.00
9060.00
9416.00
7600.00
8240.00
6144.00
6400.00
8694.00
7465.00
7050.00
6120.00
6850.00
7000.00
7012.00
6900.00
salary of
firemen
IIA4
6569.00
5640.00
6758.00*
7002.00
67sa.no*
5922.00
8406.00
6000.00
6600.00
6610.00
7310.00
79SO.OO
5590.00
6402.00
6427.00
5791.00
6612.00
5200.00
5536.00
6000.00
6672. 00
6858.00
7869.00
6758.00 •
7011.00
7122.00
6758.00*
5100.00
8619.00
8113.00
9222.00
5432.00
H424.00
6200.00
5844.00
5160.00
6408.00
6758.00*
7440.00
5010.00
6413.00
7843.00
6356.00
7345.00
5831.00
7616.00
6984.00
S3S2.00
7*86.00
5364.00
5520.00
7190.00
7058.00
5880.00
650R.OO
8340.00
5928.00
8372.00
5568.00
6400.00
7381.00
7705.00
7476.00
8664.00
7500.00
6363.00
5040.00
6500.00
7005.00
6756.00*
8844.00
8760.00
7200.00
7940.00
6504.00
6400.00
8694.00
7320.00
7200.00
6120.00
6450.00
TOOO.OO
7012.00
6900.00
Total
enploynent/
1,000 pop.
IIA5
15.80
8.10
7.80
22.20
14.90
15.60
8.70
16.40
9.10
27.50
27.40
9.90
lb.10
13.10
11.50
41.60
14.00
9.90
16.60
10.00
6. HP
10.60
13.40
8.20
8.80
12.30
10.10
13.80
20.30
10.00
9.80
14.90
10.10
12.30
7.80
16.70
13.20
10.10
14.60
33.60
9.90
15.60
9.50
21.70
9.40
6.60
20.80
21.70
25.00
10.60
14.10
28.10
24. 20
JO. 80
21.30
6.80
13.40
6.70
9.10
10.40
6.00
8.60
6.20
10.70
8.30
9.10
10.30
9.70
9.70
25.90
6.70
17.70
29.90
8.70
7.80
12.8(1
5.80
23.70
14.60
8.90
7.70
31.10
31.30
6.80
Police
enployracn
1,000 pop
HAn
2.'0
1. 'n
1.6'
1.70
Z.5(J
1.70
2.80
4.4(1
1.7(1
2.70
3.20
2. 1C
2.P'
2.3C
2.0C
2.2'
1.5(
1.91
1.61
1.3'
I.4C
1.61
1.41
1.51
1.91
1.71
2.11
2.51
2.2i
1.7i
2.1
2.5
1 .V
3.3i
l.(,
1.9
2.4
1.8
2.7
2.0
2.2
2.1
3.1
2.2
1.7
.9
2.1
2.1
2.1
2.1
2.5
3.4
2.8
1.5
2.f
1.5
2.1
2.1
?.,
2. *
l.t
l.<
1.1
2.(
1..
2.;
2.1
2.1
1.
2..
2.1
1.'
3.'
1.'
1.'
2.1
l.i
2.'
3.
1.
1.
4.
2.
2.
248
-------
TABLE B-2 (Concluded)
US
066
067
06«
069
070
071
072
07 I
07»
07>i
076
07 '
07H
07"
Old
OH
08
08
09
091
09<-
091
094
095
096
09 /
098
099
100
101
102
103
104
105
lot
107
10H
109
110
111
112
113
114
115
117
118
119
l?n
121
122
123
124
125
126
127
I**
129
130
131
132
133
134
135
136
137
13H
139
140
141
142
143
144
145
146
147
148
Fire
employment/
1,000 pop.
I1A7
1.40
1.50
1.10
1.90
7.10
1.40
I. HO
I. 10
1.70
2.90
3.00
1.70
2.3U
2.40
1.40
I. 40
1.20
1.60
1.30
1.30
1.30
1.20
l.SO
1.30
1.7U
1.70
1 .90
1 .HO
1.60
1.50
1.50
2.2U
1 .60
l.SO
2.00
1.60
<>.10
2.4o
2.30
2.10
2.00
2.00
2.00
3.00
1.70
1.20
7.511
1.60
l.t>0
2.00
2.50
?.90
1.00
1.10
2.10
.90
1.90
1.30
1.60
1.10
1.30
1.60
1.10
1.40
1.30
2.40
1.60
2.30
l.BO
1.80
l.BO
2.10
2.70
1.00
l.SO
2.70
1.30
2.30
1.90
l.SO
l.BO
2.60
2.70
1.30
unemployment
rites
IIA8
3.»0
4,00
4.80 .
3.20*
2.10
.70
5.10.
3.00
2.30
2.90
5.40
2,50
2.20
2.40
1.10
i.eo
i .40 •
2.70«
2.00
1.40
2.30 •
1.10
3.90
<-.oo
2.20
S.20.
2.40
2.40*
6.00
1.60
6.70
1.60
2.70
1.30
3.70
3.10*
l.*0
4.00
3.50
1.50
1.10
4.90
4.00«
6.00
1.00
2.60
6.50
i.eo
1.40
2. HO
3.10»
4.00
4.30 •
2.00
2.10*
5.10*
2.10.
1.70
2.40*
1.60
2.90
3.20
5.10 •
S.10 •
5.10«
4.70
2.70
3.20
6.00
2.60
«. 90
a. 40
2.70
4.20 •
2.30
4.50
5.10 *
7.50
2.10.
S.70
4.30
2.70
3.60
1.40
Violent
Crime
rate/
100,000 pop.
IIB1
397.70
7H5.40
437.90
50. 80
350.40
540.60
J70.10
774.50
471.60
69.10
201.70.
299.00
497.10
173.00
574.90
470.00
32-. 60
70"3.bO
271.20
463.20
251. BO
196.40
79.70
360.40
223.70
161.70
437.70
553. bO
543.90
208.50
330. BO
452.10
299.40.
204.90
24H.OO
1S2.60
227.90
63.30
567.90
203.90
73.70
307.80
506.90
350.40*
54B.90
208.40
350.40.
318.90
116.60
33B.70
313.20.
17B.80.
1H9.50.
363.10
422.70
249.90
447.20
486.50
413.60
116.60
192.10
700. BO
306. BO
204.20
260.00
110.40
327.70
297.40
170.60
201.70"
512.60
324.70
570.00
351.10
331.10
66.70
300.30
17B.BO.
655.10
247.00
50.10
359.50
209.70*
132.40
Property
Crime
rate/
100,000 pop.
IIB2
2431.80
5124.90
4552.10
1410.20
1469.70
2575.60
3B52.90
4454.70
2057.30
1252.90
2762.80*
1875.10
2582.70
16B9.30
2535.00
2973.00
3125.50
2946.80
1632.90
3349.60
1787.90
2368.20
1916.70
2739.70
1748.50
3682.60
2114.60
2973.70
2878.10
2867.80
4988.00
3168.50
2061.70 •
1305.00
154H.OO
1729.60
2059.90
646.50
3006.70
1676.30
869.50
3660.10
4225.40
3053.60*
3070.20
1754.30
3053.60 «
3005.70
2769.30
2303.50
1529.00.
2662.50.
204S.60"
2029.10
3175.40
3551.00
3207.60
2132.70
2218.50
884.30
1798.00
3177.60
3257.50
3087.40
3970.80
943.40
1992.40
2565.10
2700.20
2762.80.
4957.50
2910.00
3284.80
2940.50
2960.20
988.10
34V9.BO
2662.50.
3773.40
2886.20
935.60
2788.70
3408.90-
1328.00
Local
govt .
revenue
per capita
IIB3
324.86
287.17
286.58
544. 23>
214.34
160.06
512.92
250.32
358.42
432.91
288.91
218.24
158.88
209.17
309.42
361.62
415.79
166.49
225.91
300.78
278.29
314.12
435.77
223.05
228.31
365.67
262.22
217.39
370.89
275.99
502.43
177.33
305.29
251.02
169.41
308.44
238.70
206.96
285.04
304.13
220.94
382.69
399.78
329.09
216.10
267.70
322.97
268.76
337.93
21 7.69
191.46
29H.21
306.90
215.10
290.69
412.90
262.83
264.18
267.46
236.64
259.23
297.25
392.57
402. S3
758.58*
183.06
230.68
287.26
269.21
386.76
542.18
365. 9S
306.69
313.27
255.12
346.74
384.61
248.67
402.19
327.72
156.75
297.81
342.90
200.35
X of
revenue
from
federal
govt.
IIBA
2.70
6.10
.10
11.00.
6.50
2.40
2.40
.60
1.60
4.1(1
4.50
.50
8.40
3.40
3.40
3.70
5.70
2.10
5.80
3.80
2.30
2.20
1.50
2.30
1.10
8.80
4.50
7.80
1.30
1.30
2.10
.50
2.00
3.40
3.20
7.60
1.20
2.00
.70
5.30
3.10
2.60
4.60
4.40
6.80
1.50
2.70
6.50
1.50
5.40
3.30
7.80
7.10
7.10
1.70
2.20
2.90
.80
1.70
2.70
.20
1.20
3.50
3.90
15.70.
6.40
2.30
2.40
2.70
1.40
5.20
3.80
1.40
3.50
2.90
4.10
5.90
2.00
1.00
4.20
.60
1.90
1.10
.60
Per capita
local govt.
Expend .
on public
welfare
1IC1
11.88
.01
6.97
40.00*
1.49
2.90
-i0.6»
.06
2.02
27.32
1.9R
8.83
.83
1.94
18.94
1.40
31. 2H
.50
1.24
2.51
6.18
5.64
59.24
.88
7.97
.24
15.87
16.08
12.47
15.01
99.87
2.48
11.22
10.33
7.39
.37
.71
6.79
10.61
3.31
5.64
10.74
4.18
54.73
.51
11.21
62.45
4.84
27.36
.04
.44
4.53
3.16
10.20
4.93
33.09
1.19
4.21
18.70
7.33
4.26
6.46
39.50
35.76
66.25*
6.74
.02
19.67
.32
7.95
80.94
.13
22.77
.16
.61
38.07
49.54
3.08
6.85
31.16
B.33
6.79
49.84
10.37
»vg.
monthly
retiree
benefits
IIC2
132.00
129.00
148.00
140.00
116.00
125.00
129.00
128.00
139.00
138.00
150.00*
144.00
116.00
138.00
127.00
125.00
127.00
119.00
114.00
121.00
139.00
140.00
134.00
121.00
140.00
136.00
131.00
10B.OO
149.00
144.00
125.00
121.00
141.00
134.00
127.00
109.00
115.00
137.00
147.00
126.00
138.00
145.00
134.00
140.00 »
119.00
145.00
140.00*
111.00
142.00
120.00
114.00
148.00 *
141.00 •
129.00
127.00
135.00
113.00
142.00
121.00
137.00
147.00
144.00
132.00
135.00
133.00
131.00
118.00
146.00
133.00
150.00*
127.00
137.00
144.00
135.00
134.00
137.00
126.00
148.00*
139.00
138.00
134.00
141.00
139.00 .
134.00
Avg. monthly
payments
to families
w/dependent
children
IIC3
190.00
120.00
267.00
226.00
96.00
104.00
252.00
86.00
113.00
212.00
259.00.
154.00
77.00
110.00
122.00
104.00
179.00
77.00
98.00
113.00
209.00
202.00
207.00
120.00
229.00
165.00
136.00
123.00
240.00
151.00
237.00
76.00
147.00
225.00
122.00
60.00
56.00
207.00
228.00
102.00
222.00
2<8.0n
118.00
257,00.
96.00
163,00
257.00*
105.00
260.00
61.00
56.00
269.00*
247.00 •
178.00
90.00
200.00
80.00
210.00
122.00
209.00
228.00
239.00
240.00
231.00
219.00
216.00
87.00
154.00
209.00
259.00 •
253.00
220.00
249.00
117.00
144.00
229.00
246.00
269.00 •
94.00
172.00
206.00
137.00
219.00*
207.00
249
-------
TABLE B-3
BASIC STATISTICS OF ENVIRONMENTAL COMPONENT (M)
6,6 ALB
f>7 ANN
68 APP
69 AUG
70 »Ub
71 B*K
72 BAT
73 BE*
74 WIN
75 8R1
76 CAN
77 CHA
78 CHA
79 CHA
60 CHA
81 COL
82 COL
83 COL
84 CON
85 OAV
86 OES
87 DUL
88 ELP
89 ERI
90 EU«
91 LVA
92 FAY
93 FLI
94 FOX
95 FHE
96 GHE
97 HAM
98 HAR
99 HUN
100 HUN
101 JAC
102 JOH
103 KAL
104 KNO
105 LAN
106 LAN
107 LAS
108 LAD
109 LIT
110 LOR
111 LOW
112 MAC
113 MAD
114 MOB
115 MON
116 NEW
117 NEK
118 NEW
119 ORL
120 OXN
121 PEN
122 PEO
123 RAL
124 REA
125 HOC
126 SAG
127 SAL
128 SAN
129 SAN
130 SCO
131 SHR
132 SOU
133 SPO
134 STA
135 S70
136 TAC
137 TRE
138 TUC
139 TUL
140 UTI
141 VAL
142 WAT
143 WES
144 WIC
145 W1L
146 WIL
147 WOR
148 VOR
Man Level
tot Total
Suspended
Partlculacaa
IA1
92.33
73.95
91.63*
62.86*
69.74
135.19*
61.22
59.98
57.77
57.24
102.62
46.72
104.65
98.97
105.55
96.56
62.86
50.08
103.71
127.16
85.23
71.51
142.42
104.43
"5.52*
75.25
65.18
130.10
75.41
114.98
76.65
81.28
77.44
96.35
63.06
105.33*
102.76
58.93
99.42
107.58
77.93
100.42
65.08
73.88 •
200.26 •
50.31
81.63 •
73.84
106.27
96.99
59.72
61.81 •
53.84
75.40 •
118.49 •
106.27 •
77.71
54.91
117.29
105.13*
130.10 •
114.98*
118.49*
118.49 •
188.75
105.33
75.41
97.91
57.24 •
59.83*
93.89
71.05
98.08
83.13
71.67
59.83*
80.53
61.80*
142.33
127.44
125.19
72.43
84.58
Mean Level
for
Sulfur
Dioxide
IA2
56.00
308.00 •
124.00 •
16.00
16.00
19.00 *
91.00
52.00
67.00*
90.00
173.00
18.00
120.00
203.00
92.00
26.00 •
62.00
87.00
10.00
28.00 •
28.00
66.00
119.00
106.00
99.00 •
97.00
52.00
71.00
56.00
12.00
55.00
24.00
14.00 •
120.00 •
105.00 •
12.00
4.00
43.00
47.00
89.00
62.00
19.00 •
293.00
18.00
113.00 •
414.00
16.00
37.00
71.00
25.00
75.00
75.00 *
14.00
73.00 .
52.00 •
71.00 «
126.00
51. '00
141.00
23.00
38.00
12.00 •
52.00 •
52.00*
93.00
143.00
152.00
19.00
90.00*
36.00*
73.00
74.00
12.00
464.00
67.00
36.00*
33.00
12.00*
22.00
141.00*
104.00
210.00
U.OO
Mean
Annual
Inversion
Frequency
IB1
37.50
32.50
32.50
42.50
22.50
37.50
32.50
27.50
32.50
22.50
27.50
37.50
42.50
47.50
37.50
37.50
42.50
37.50
22.50
32.50
32.50
27.50
37.50
22.50
32.50
32.50
32.50
32.50
32.50
42.50
47.50
27.50
32.50
42.50
37.50
32.50
32.50
32.50
42.50
32.50
32.50
47.50
27.50
37.50
22.50
27.50
37.50
32.50
32.50
37.50
22.50
22.50
22.50
32.50
37.50
32.50
32.50
32.50
32.50
32.50
32.50
37.50
37.50
37.50
32.50
32.50
32.50
37.50
22.50
42.50
32.50
22.50
42.50
37.50
32.50
37.50
27.50
17.50
37.50
32.50
22.50
27.50
32.50
1 of
Housing Units
Dilapidated
IB2
2.30
1.60
1.40
3.40
2.20
3.20
2.80
3.40
3.20
3.90
1.50
2.90
2.20
2.30
2.50
1.70
2.30
3.30
3.10
1.30
1.20
1.30
1.60
3.50
2.30
2.20
2.30
1.80
1.50
2.80
2.70
1.70
3.90
2.00
1.80
3.10
3.60
1.50
2.10
3.30
l.SO
1.90
4.50
2.30
1.50
4.00
3.20
1.30
3.50
3.10
4.20
3.90
2.20
2.30
2.30
2.80
1.50
2.00
3.20
1.70
2.20
2.90
2.20
2.80
3.30
3.20
1.70
2.10
3.40
2.70
2.10
4.70
2.50
2.00
3.30
2.30
4.60
2.80
2.30
3.50
1.70
4.60
3.50
Park and
Acres/
1,000 Pop.
IB3
S.70
60.10
12.50
3.60
7.70
64.20
7.30
4.80
5.80
11.00
4.90
5.40
8.60
2.60
6.60
16.40
6.40
11.40
53.00
26.60
12.90
45.50
3.70
3.70
53.50
10.90
1.50
27.40
4.90
1133.00
19.40
10.80
7.40
20.50
11.50
17.70
109.90
22.80
236.80
2.00
13.90
2680.10
19.60
15.40
17.40
14.70
5.20
6.50
21.60
27.10*
7.60
16.30
34.10
19.60
32.20
5.90
27.70
26.80
44.60
27.60
3.50
154.80
16.70
78.90
3.10
9.50
5.70
101.30
7.70
1.00
483.30
5.00
41.10
57.10
21.60
136.20
29.50
5.40
17.80
49.90
7.40
13.30*
28.50
Pop.
In Central
City
Id
2965.00
4578.00
49JJS.OO
3938.00
3492.00
2684.00
4108.00
1489.00
5829.00
9723.00
5792.00
3892.00
2629.00
3173.00
2268.00
2221.00
3399.00
2218.00
2033.00
3022.00
3174.00
1264.00
2724.00
6638.00
2925.00
3B55.00
2287.00
5894.00
3450.00
3971.00
2957.00
3313.00
6955.00
4562.00
1263.00
3067.00
7452.00
3492.00
2267.00
N013.00
3939.00
2436.00
2891.00
2449.00
3307.00
6929.00
2498.00
3572.00
1630.00
2875.00
7484.00
2269.00
2092.00
3600.00
3640.00
2*79.00
3395.00
2708.00
8853.00
4309.00
5309.00
4019.00
33*4.00
2513.00
4030.00
3200.00
4301.00
3357.00
2856.00
3600.00
3241.00
13952.00
3287.00
2369.00
2704.00
3629.00
3914.00
4182.00
3197.00
7086.00
6231.00
4721.00
9497.00
Motor
Vehicle
Registrations/
1,000 Pop.
IC2
654.00
494.00
472.00
532.00
565.00
643.00
5X4.00 «
594.00
470.00
603.00 •
565.00
453.00
500.00
629.00
552.00
609.00
516.00
509.00
570.00
574.00
621.00
530.00
512.00
4X1.00 •
615.00*
5X5.00
431.00
523.00
556.00
632.00
575.00
549.00
481.00 •
509.00
617.00
573.00
481.00*
520.00
5*6.00
*ftl .00 •
518.00
698.00
481.00 •
589.00
572.00
4X1,00 •
5*7,00
494,00
561.00
572.00
553,00*
574,00*
456.00
701,00
594.00
595.00
562.00
741.00
4X1.00 •
5*8.00 •
517.00
5*9.00
600.00
684.00
481.00*
5X4.00*
530.00
652.00
603.00*
623.00
573,00
481.00*
656.00
672.00
444.00
604.00
553.00<>
704.00
655.00
4BUOO"
57"). 00"
4Xl.OO'>
481.00"
Registrations/ Generated by
1,000 Pop.
IC3
32.00
25.00
13.00
8.00
23.00
43.00
18.00 •
14.00
8.00
10.00 •
12.00
12.00
33.00
17.00 •
15.00
28.00
8.00
17.00
13.00
20.00
31.00
26.00
16.00
12.00
36.00
19.00
17.00 •
26.00
16.00
2V. 00
7.00
15.00
ia. oo
18.00
21.00
16.00
12.00
24.00
If,. 00 •
12.00
29.00
36.00
10.00 •
U.OO
15.00
10.00 »
16.00
16.00
14,00
U.OO
lo'.oo •
10.00 •
17.00 •
19.00
35.00
17.00
17.00
17.00 •
12.00
13.00
24.00
25.00
34.00
34.00
12.00
18.00 •
15.00
24.00
10.00 •
27.00
19.00
10.00 •
24.00
29.00
7.00
32.00
10.00 •
24.00
34.00
12.00
17.00 •
10.00 •
12.00
Manufacturing
ID
668.40
301.10
550.40
445. BO
1172.50
664.60
295.40
222.80
629.70
443.50
502.70
1078.60
213.20
7?7.70
532.60
H05.20
914.00
658.20
339.30
443.20
450.00
712.20
713.30
531.20
660.90
42i. on
1071.40
514.80
464. 00
532.20
750.80
603.10
584 .40
4*8.70
692.10
620.00
76*?. *0
393.20
56*. 90
S70.80
304.70
480.40
677.50
f>v7.VO
bHO .90
Bib. 60
S6b. 20
M5.40
722.00
853.90
481.90
531 .10
713.00
62V. 70
1107.80
451.90
455.60
71V. 70
737.70
*h2.60
404.50
465.90
H26.00
820 .60
875.00
840.70
618.50
476.70
3X9.20
376.00
645.40
513.40
788.40
571.50
*U5.40
11X0.90
561.40
665.70
420.40
906.60
557.90
577.40
669.90
Pollution
Index
IE
.79
1.10 •
1.50
llll
f . bs
4. Mfc
• V6
«>.6S
5.33
2.0--
12. ft-
2.7.H
.80
.91
2.8*
.81
1.0-,
.8h
.X4
1.01
l.lu
1.4?
1.07
7.71
.74
1.34
7.30
.S-,
2. SI
3.07
2.44
V.2b
.7J
1.25
b.87
1.44
1.1-
1.50
1.7h
.97
1.07
1.47
*.**
1.54
2.3h
.5^
9.43
.«V
2. ho
3.J*
.73
1.4b
.60
3.77
1.67
2.27
1 . 9fc
1.04
1.86
1.13
.Sv
.7* •
1.6,?
7.46
1.40
,7C
2.1*
1.53
1.07
.60
1.22
3.50
2.02
1.10
1.88
13.89
.79
1.16
.77
2.22
3,69
250
-------
TABLE B-3 (Concluded)
66 ALB
67 ANN
68 APP
61 AUG
70 «US
71 BAK
72 B»T
73 BEA
74 BIN
75 BRI
76 CAN
77 CMA
76 CHA
79 CHA
80 CHA
81 COL
82 COL
83 COL
8* COR
85 OAV
86 DES
87 DUL
88 ELP
89 ERI
90 EUG
91 EVA
9? FAY
93 FLI
94 FOR
95 FRE
96 ORE
97 HAM
98 MAR
99 HUN
100 HUN
101 JAC
102 JOH
103 KAL
104 KNO
105 LAN
106 LAN
107 LAS
108 LAW1
109 LIT
110 LOR
111 LOIr
112 MAC
113 MAD
11* MOB
US «ON
116 NEW
117 N£«
118 NCH
119 ORL
120 OXN
121 PEN
122 PEO
123 RAL
12* REA
125 ROC
126 SAG
127 SAL
128 SAN
129 SAN
130 SCR
131 SMR
132 SOU
133 SPO
13* STA
135 SIO
136 TAC
137 THE
138 TUC
139 TUL
140 UTI
141 VAL
142 MAT
1*3 »ES
1*4 UK
14$ «IL
146 K1L
147 VOR
148 TOR
Mean
Annual
Inversion
Frequency
I1A1
37.50
32.50
32.50
42.50
22.50
37.50
32.50
27.50
32.50
22.50
27.50
37.50
42.50
47.50
37.50
37.50
42.50
37.50
22.50
32.50
32.50
27.50
37.50
22.50
32.50
32.50
32.50
32.50
32.50
42.50
47.50
27.50
32.50
42.50
37.50
32.50
32.50
32.50
42.50
32.50
32.50
47.50
27.50
37.50
22.50
27.50
37.50
32.50
32.50
37.50
22.50
22.50
22.50
32.50
37.50
32.50
32.50
32.50
32.50
32.50
32.50
37.50
37.50
37.50
32.50
32.50
32.50
37.50
22.50
42.50
32.50
22.50
42.50
37.50
32.50
37. SO
27.50
17.50
37.50
32.50
22.50
27.50
32.50
Possible
Annual
Sunshine
Days
IIA2
77.00
54.00
53.00 •
64.00 •
61.00
83.00 •
60.00 •
59.00 •
51.00
61.00 •
52.00 •
63.00
48.00 •
66.00
58.00
70.00 •
64.00
59.00 •
64.00
58.00 •
60.00
55.00
83.00
53.00 •
47.00 •
63.00
61.00 •
54.00 •
58.00
83.00
59.00
57.00 •
58.00
48.00 •
58.00 •
60.00
52.00 •
51.00 •
56.00
57.00 •
54.00
86.00
60.00 •
62.00
52.00
60.00 •
62.00
58.00
60.00
59.00
61.00
61.00
63.00
64.00 •
73.00 •
60.00
58.00
61.00
57.00
se.oo •
54.00 •
67.00 •
73.00 •
67.00 •
53.00
64.00
57.00 •
57.00
61.00 •
79.00 •
48.00
59.00
86.00
62.00
51.00 •
67.00
57.00
64.00 •
65.00
53.00
58.00
57.00 •
57.00 •
No. of
Days With
Thunder-
storm*
IIA3
43.00
34.00 •
29.00 •
77.00
48.00
3.00
80.00
75.00
38.00
24.00
40.00
58.00
51.00
45.00
60.00
56.00
67.00
62.00
33.00
45.00
54.00
38.00
22.00
41.00
1.00
46.00
48.00 •
30.00
42.00
8.00
46.00
48.00 •
28.00
46.00
71.00
76.00
38.00*
36.00*
48.00
28.00*
34.00
IS. 00
17.00*
77.00
40.00
17.00
52.00
39.00
95.00
81.00
24.00 •
24.00*
24.00 •
85.00
1.00
84.00
56.00
48.00
28.00*
43.00
34.00*
2.00*
1.00*
2.00*
31.00
62.00
42.00
5.00
24.00*
5.00
4.00*
33.00
28.00
70.00
33.00*
Z.OO*
21.00'
92.00
S3. 00
31.00
27.00
24.00
2«.00*
No. of
Days With
Tenp. 90"
or Above
IIA4
62.00
14.00 •
12.00 •
62.00
98.00
123.00
90.00
50.00
0.00
10.00
10.00
60.00
22.00
25.00
37.00
IT. 00
64.00
70.00
102.00
21.00
23.00
2.00
108.00
0.00
16.00
39.00
17.00 •
9.00
17.00
106.00
24.00
21.00 •
24.00
30.00
35.00
83.00
17.00 •
14.00*
10.00
24.00 •
14.00
141.00
19.00 •
60.00
11.00
19.00
96.00
13.00
98.00
98.00
10.00 •
10.00 •
10.00 •
113.00
21.00*
82.00
12.00
17.00
24.00 •
13.00
14.00*
6.00*
21.00*
6.00
6.00
57.00
14.00
29.00
10.00*
80.00
1.00 •
24.00
135.00
52.00
15.00*
6.00*
30.00*
70.00
61.00
6.00
24.00
5.00
24.00*
No. of
D«y» With
Temp. 32°
or Below
IIA5
128.00
130.00 •
138.00 •
56.00
18.00
4.00
21.00
25.00
126.00
89.00
105.00
16.00
93.00
69.00
72.00
168.00
62.00
42.00
13.00
107.00
107.00
188.00
73.00
111.00
47.00
86.00
76.00 •
119.00
115.00
11.00
61.00
101.00 •
95.00
89.00
54.00
40.00
101.00 •
124.00 •
60.00
95.00 •
130.00
36.00
76.00 •
56.00
94.00
76.00
42.00
132.00
21.00
37.00
89.00 •
89.00 *
89.00 •
2.00
0.00 •
15.00
104.00
76.00
95.00 •
109.00
130.00 •
2.00 •
0.00 •
2.00
120.00
32.00
94.00
131.00
89.00 •
9.00
17.00 •
63.00
18.00
64.00
111.00 •
2.00 •
113.00 •
o.oo
100.00
120.00
76.00
128.00
95.00 •
Park and
Recreation
Acres/1.000
Pop.
IIB1
5.70
ftO. 10
1^.50
3.60
7.70
64.20
7.30
4.80
5.80
11.00
4.90
5.40
8.60
2.80
6.60
16.40
6.40
11.40
bS.OO
26.60
12.90
45.50
3.70
3.70
53.50
10.90
1.50
27.40
4.90
1133.00
19.40
10. HO
7.40
20.50
11.50
17.70
109.90
22.80
236.60
2.00
13.90
2680.10
19.60
15.40
17.40
14.70
5.20
6.50
21.60
27.10 •
7.60
16.30
34.10
19.60
32.20
5.90
27.70
26.80
44.60
27.60
3.50
154. BO
16.70
78.90
3.10
9.50
5.70
101.30
7.70
1.00
483.30
5.00
41.10
57.10
21.60
136.20
29. SO
5.40
17.80
49.90
7.40
13.30*
28.50
Miles of
Trails/
100,000
Pop.
IIB2
146.70
192.30
1 1 1.90
197.60
70.90
1164.10
10. SO
50.60
56.10
92.50
34.90
131.60
243.50
0.00
114.40
288.10
71.20
62.80
7.00
322.30
97.90
1101.80
136.40
53.00
3535.20
201.70
47.10
175.00
S2.10
4401 .90
70.00
43S.OO
878.30
118.10
276.30
0.00
614.90
49.50
3SO.OO
78.10
04.60
688*60
500.00
108.30
73.90
267.60
43.60
48.20
21.20
237.50
73.00
33.60
191.70
144.80
2287.20
164.60
67.20
192.90
429.00
213.20
63.60
1840.00
3102.20
404.80
59.80
128.80
53.50
512. 10
48.50
17.20
1214. 10
3.20
1758.50
33.50
8.80
409.60
224.80
120.30
38.50
230.90
6H.10
261.90
360.60
251
-------
TABLE B-4
BASIC STATISTICS OF HEALTH AND EDUCATION COMPONENT (M)
us
66 ALB
67 ANN
66 APP
69 AUG
TO »US
71 BAR
72 BAT
73 BE*
7» BIN
75 BI-I
76 CAN
77 CMA
76 CHA
79 CHA
60 CHA
81 COL
82 COL
63 COL
84 COR
85 DAV
86 DEE
87 OUL
88 tLP
89 EK1
90 EUG
91 EVA
92 FAT
93 FLI
9* FOR
95 FRE
96 GRE
97 HAM
98 HAD
99 HUN
100 HUN
101 JAC
102 JOH
103 K»L
104 KNO
105 LAN
106 LAN
107 LAS
108 LAM
109 LIT
110 LOR
111 LOH
Hi MAC
113 HAD
114 MOB
115 HON
116 NEK
117 NEK
118 NE«
119 ORL
120 OXN
121 PEN
122 PEO
123 PAL
124 REA
125 ROC
126 SAG
127 SAL
128 SAN
129 SAN
130 SCR
131 SH»
132 SOU
133 SPO
134 STA
135 STO
136 TAC
137 THE
138 TUC
139 TUL
140 UTI
141 VAL
142 HAT
143 «es
144 OIC
145 MIL
146 VIL
147 DOR
148 VOR
Infant
fertility
Rite/1,000
Live Births
IA1
21.20
20.70
22.10
19.00
25.40
18.70
21.60
19.80
21.30
16.20
18.10*
19.00
25.60
22.00
22.20
24.00
28.30
23.60
23.40
21.00
27.00
19.20
19.00
17.40
19.60
14.40
19.40
25.30
22.60
22.00
20.40
25.20
15.40
21.20
17.90
19.20
28.00
20.00
22.10
21.50
18.10
16.80
27.20
20.10 •
21.30
22.70
20.10*
23.30
14.20
22.80
31.20
20.80*
21.80*
23.50
26.10
23.80
24.20
23.90
21.80
21.00
18.50
23.20
20.70
17.50
20.00
20.00
24.10
20.20
19.60
18.10*
15.50
21.60
25.80
17.50
19.60
18.10
23.40
20.80*
28.00
22.60
19.10
19.20
21.20 •
17.10
Death Kite/
1,000 pop.
IA2
9.50
6.30
S.90
7.50
8.60
6.50
8.30
7.30
8.20
9.50
8.70 •
9.90
7.50
10.20
7.80
9.70
S.70
7.30
7.90
6.90
9.60
9.10
11.40
6.20
10.20
6.90
10.60
5.20
7.50
A. 10
8.60
8.60
8.10
9.90
10.40
6.30
8.50
11.60
7.30
8.90
8.60
7.00
6.70
8.10*
9.30
8.20
8.10*
9.00
6.90
8.60
10.70
9.40*
8.20.
6.50
G.60
6.10
7.30
9.10
7.30
11.00
a. 10
8.30
6.90
7.50
10.70
13.50
9.70
9.40
10.40
8.70*
9.40
6.60
9.60
8.40
9.00
10.80
8.00
9.40 •
11.60
8.00
12.90
6.80
10.30*
9.60
Median
Schoola
Years
Completed
IBI
12.10
12.50
12.60
12.20
11.50
12.40
12.10
12.30
11.60
12.20
12.10
12.10
11.80
12.10
12.00
11.60
12.60
12.00
11. SO
11.50
12.10
12.40
12.20
12.00
12.20
12.30
12.10
12.20
12.10
12.20
12.10
10.90
11.70
12.10
11.40
12.30
12.20
10.90
12.30
12.00
11.10
12.40
12.40
12.10
12.20
12.10
12.10
11.60
12.60
11.00
12.10
12.20
12.10
12.10
12.20
12.40
12.00
12.10
12.20
11.10
12.10
12.00
12.40
12.60
12.40
11.70
12.00
12.10
12.40
12.60
11.90
12.30
12.10
12.40
12.20
12.00
12.30
12.00
12.20
12.40
11.50
12.10
12.10
11.20
1 of Persons
25*. Completed
4yrs. High School
or toore 1B2
52.30
66.20
67.50
56.20
46.30
60.90
51.70
59.10
46.30
58.90
51.90
52.40
48.40
52.60
50.60
47.60
72.90
50.60
46.60
47.10
55.60
68.00
56.20
51.10
58,40
61.90
52.00
55.10
52.30
59.40
52.70
41.00
47.80
55.40
46.10
58.30
56.10
»». 10
60.70
50.70
43.90
63.10
65.20
53.70
56.50
52.60
54.50
47.40
71.20
42.30
51.60
56.80
54.10
52.10
56.10
63.80
51.00
S3. 70
S3. 60
• 3.30
S2.20
50.60
62.50
71.30
63.60
48.00
50.90
54.20
65.30
68.10
49.40
60.70
52.60
63.10
58.20
49.90
62.90
49.90
55.70
63.20
46.90
54.40
SI. 70
44.70
1 of Males
16-71
not High
School
Graduates
IB )
15.20
1KOO
9.10
6,, 20
22. .60
11 ,,60
17,10
13,00
11,50
10.40
11.50
12.80
19.10
15.20
17.20
21.10
19.50
21.00
26.20
19.20
9.70
10.70
6.70
15.70
8.30
7.10
15.30
26.30
13.80
14.50
12.90
18.70
11.40
13.00
19.50
IT. 50
IT. 10
D.60
10.80
14.50
20.80
7.50
14.00
IS. 00
l'j.80
13.00
15.40
19.70
S.10
IB. 40
23.80
10.50
11.20
18.90
18.30
13.80
15.90
11.90
14.30
14.50
16.70
13.20
22.70
6.40
12.10
10.90
15.60
14.00
6.90
6.70
14.20
i'0.00
11.00
10.90
13.20
12.20
9.10
16.00
l». JO
11.10
12.90
11. SO
11.60
13,50
54.30
55.90
61.10
S9.10
45.90
54.50
SS.10
5S.60
57.00
5S.10
57.50
53.80
48.30
52.40
bl.OO
49.80
45.90
48.30
42.50
53.10
55.10
S2.60
59.70
52.90
56.50
59.80
S3.10
37.00
55.50
54.90
59.50
50.60
S6.60
S4.30
49.90
51.30
54.40
57.10
59.10
53.00
52.80
60.10
47.70
54.90
46.60
55.10
54.60
50.90
59.00
54.00
52.90
58.20
52.10
49.40
55.00
56.90
50.00
56.10
S4.20
54.40
53.20
55.70
47.20
S8.00
55.20
56.50
53.00
57.40
57.00
62.60
57.30
49.20
57.20
56.70
53.10
SS.60
52.00
56.50
51.10
55.30
54.70
S4.90
S9.40
52.40
252
-------
TABLE B-4 (Concluded)
us
66 ALB
67 ANN
68 »PP
61 AUG
TO AUS
71 BAK
12 BAT
73 BE*
74 BIN
75 8RI
76 CAN
77 CH»
78 CHA
79 CHA
80 CHA
81 COL
82 COL
83 COL
8* COR
85 DAV
86 OES
87 OOL
88 ELP
89 EMI
90 CU6
91 EVA
92 FAr
93 FLI
9* FOR
95 FRE
96 GRE
97 HAM
98 HAR
99 HUN
100 HUN
101 JAC
103 JOH
103 KAL
10* KNO
105 LAN
106 LAN
107 LAS
108 LAM
109 LIT
110 LOR
111 LOB
113 MAC
113 MAO
11* MOB
115 MON
116 NEK
117 NEK
118 NtH
119 ORL
120 OXN
1?) PEN
122 FIO
123 RAL
124 KEA
125 ROC
126 SAG
127 SAL
128 SAN
129 SAN
130 SCR
131 SHR
132 SOU
133 SPO
134 STA
135 STO
136 TAC
137 THE
136 TUC
139 TUL
140 UTI
141 VAL
142 MAI
143 XtS
144 «IC
145 UIL
146 nil
147 »OR
148 TOD
Dcntlata/
100,000
pop.lUl
S9.SO
53.20
111.50
53.80
55.60
56.50
38.60
53.30
41.10
53.20
80.50.
46.70
42.60
51.80
42.00
46.90
61.00
47.40
23.50
42. 50
45.20
65.40
69.70
33.40
58.00
68.90
47.30
22.20
46.10
44.90
56.20
36.10
33.20
52.80
45.30
34.60
44. BO
45.70
59.50
50.70
46.90
52.10
44.60
82.50*
46.70
42.00
82. SO*
35.40
72.00
33.70
40.70
67.80*
48.90*
39.40
53.00
52.10
34.60
44.70
53.80
54.30
47.00
46.90
68.60
81.70
77.10
62.40
52.90
52.10
73.70
80.50*
55.10
54.30
57.20
54.00
51.80
51.40
60.60
67.80*
76.60
47.30
56.40
44.40
56.30*
47.90
Hoapltal Bede/
100.000 pop.
IIA2
414.90
363.60
772.30
535.60
464.40
312.30
342.10
425.40
434.30
462.20
324.30*
401.10
400.20
619.10
443.10
435.50
307.70
409.40
347.00
441.30
488.90
517.60
764.70
419.70
397.50
296.20
634.10
183.00
382.80
519.50
393.40
331.90
523.90
463.90
593.50
416.20
493.20
552.50
368.10
535.80
321.90
348.00
299.70
466.80*
556.10
358.60
466.80 •
374.10
624.20
382.30
457.00
377.10*
244.00*
395.00
448.80
341.10
396.20
606.20
394.00
380.60
402.50
364.50
308.70
452.10
328.50
553.60
641.00
322.80
468.20
324.30*
342.90
280.80
525.10
358.90
415.80
399.40
388.60
377.10*
393.40
527.50
469.50
340.70
474.50 •
253.40
Hotpital
Occupancy
«atta IIA3
79.80
77.00
78.00
73.10
82. 3«
• 1.40
69.80
73.20
81.40
87.60
83.70*
82.80
77.10
83.70
82.60
78.90
74.90
80.60
101.50
69.60
78.10
88.60
75.90
77.20
84.00
72.00
81.70
89.80
89.70
86.40
67.30
85.00
75.80
86.40
77.20
81.40
85.00
82.30
77.90
81.90
76.70
77.20
72.20
80.00*
82.10
88.60
80.00 •
75.50
75.40
86.40
81.30
81.40*
73.40*
60.20
78.10
66.00
82.50
61.80
61.80
88.70
75.90
84.90
102.30
67.20
78.90
77.10
74.40
68.70
75.20
83.70 •
68.80
76.00
76.50
74.40
77.10
82.00
63.60
81.40*
T7.00
61.70
79.70
78.40
64.70*
81.80
Phyalclana/
100,000
pop . I IAA
153.80
216.50
557.00
99.00
217.80
162.10
107.80
142.70
105.10
150.00
187.70*
108.50
185.30
144.70
127.00
137.40
110.20
131.90
75.40
125.00
86.00
133.50
121.00
98.00
99.00
128.40
134.50
46.20
100.10
116.20
135.60
109.60
92.00
155.40
101.70
71.40
229.00
94.00
165.20
149.90
100.10
107.80
99.90
274.00*
224.60
96.90
274.00*
111.00
363.10
102.70
105.80
267.80 •
135.30 •
94.50
149.50
146.40
93.40
116.70
130.90
127.90
119.10
93.70
147.20
212.60
173.80
108.50
144.20
108.20
169.70
187.70 •
137.50
106.30
216.10
207.00
118.50
123.90
163.00
267.80 •
182.10
111.00
102.20
141.90
112.00 •
94.70
f»r Capita
Local Cov't
Expend, on
H..lth 11*5
2.96
1.70
1.01
4.16*
2.23
1.71
5.93
1.72
2.27
4.61
4.02
1.56
1.84
2.16
4.67
4.23
2.20
1.89
2.27
2.10
.84
1.96
2.05
2.62
1.21
2.11
1.60
2.23
2.84
1.27
6.90
1.76
2.02
.45
2.08
1.23
1.86
.17
1.92
1.77
.46
1.82
2.94
2.56
1.63
2.62
2.94
2.31
2.94
1.92
2.06
3.22
1.64
I.B4
.65
3.37
1.34
2.09
2.60
.70
.63
2.35
5.56
.25
8.13*
.43
1.90
1.60
2.45
3.27
9.78
2.Z7
4.05
3.32
2.69
2.59
4.20
2.29
1.49
4.05
.41
1.21
2.61
.26
Per Capita
Local Cov't
Expend, on
Educ. ^IBl
145.69
152.43
175.06
226.68*
105.28
132.93
216.27
145.98
131.11
222.52
136.73
121.86
68.52
119.83
142.27
121.88
184.42
94.23
105.90
163.98
142.52
161.59
171.10
122.75
124.67
184.50
121.46
100.51
199.60
129.62
187.01
89.06
131.49
154. 5«
104.60
103.13
102.64
113.32
153.13
114.42
157.20
193.3*
158.33
132.41
105.01
135.06
129.99
115.52
171.42
93.66
89.40
148.31
144.96
123.90
126.11
231.7*
145.95
134.75
121.97
1*1.16
131.29
1*8.52
183.43
173.92
116.3**
96. Bl
159.44
137.00
147.32
196.13
173.78
176.64
144.20
167.66
132.99
192.35
170.44
113.54
174.89
1*9.55
95.06
147.13
136.43
123.22
% of Peraona,
25+ , Completed
4 yra. College
or more IIB2
10.70
16.90
27.40
9.50
10.50
19.50
6.90
16.60
6.60
10.50
10.60
7.10
10.10
9.90
12.60
9.30
16.50
14.40
9.00
9.80
8.90
12.60
9.20
11.40
6.80
14.20
8.10
10.00
7.20
10.20
10.20
10.20
8.50
9.40
7.50
16.40
14.00
5.10
14,50
11.30
8.20
14.90
10.00
9.40
10.70
7.10
9.10
9.30
23.10
7.30
11.50
14.40
11.30
11.50
11.20
13.30
9.30
9.30
17.10
6.60
8.30
7.40
15.00
17.90
11.10
6.00
10.60
9.30
11.90
25.40
8.00
10.10
14.10
15.70
11.70
8.70
10.00
9.60
11.90
12.00
5.50
11.00
10.10
7.00
253
-------
TABLE B-5
BASIC STATISTICS OF SOCIAL COMPONENT (M)
66 ALB
67 ANN
68 APP
6*4 AUG
70 AUS
71 HAK
72 HA1
73 BEA
74 HIN
7b MKI
76 CAN
77 CHA
7H CHA
79 CHA
BO CHA
81 COL
82 COL
63 COL
84 COH
85 [IAV
86 OFS
87 DUL
8H fLP
B9 Fkl
90 t UG
91 F.V4
92 FAY
93 FL:
94 H>K
95 FHF
96 GKF
97 HAM
98 HAH
99 HUN
100 HUN
101 JAC
102 JOH
103 KAL
10* KNO
lOb LAN
106 LAN
107 LAS
108 LAM
109 LIT
110 LOR
111 LOU
112 MAC
113 MAD
114 MOH
115 MON
116 NFU
117 NtN
lie HE*
119 OBL
120 OXN
121 PEN
122 PFU
123 HAL
124 WE A
12b HOC
126 SA(,
127 SAL
128 SAN
129 SAN
130 SCH
131 SCH
132 SOU
133 SPO
134 STA
135 STO
136 IAC
137 THE
136 IUC
139 TUL
MO UTI
1*1 VAL
14? HAT
143 VES
144 «1C
145 (-IL
146 »IL
1*7 HOB
148 VOX
Labor Force
Participation
Rate (1)
IA1
66.00
61.70
6S.60
68.40
55.40
64.20
60.30
61.50
63.50
68.80
71.70
66.60
S3. 00
60.60
72. BO
67.60
44.10
S7.50
50.30
61.20
70.00
73.70
64.70
54.60
66.50
64.10
68.60
36.50
65.30
72.00
61.90
69.40
63.50
70.10
57.90
62.30
65.80
59.00
66.40
62.20
74.50
67.40
67.10
76.10
64.90
66.40
71.70
64.20
70.20
61.80
65.00
70. BO
60.00
54.40
65.20
64.20
53.00
69.20
67.60
73.90
73.00
65.30
52.80
62.00
61.90
68.30
62.70
69.50
62.90
70.10
64.20
52.70
69.80
58.40
68.90
66.70
55.90
72.40
69.40
69.10
66.80
66.40
71.40
72.80
X of
Labor Force
Employed
Tj^2
95.60
94.50
95.00
96.70
96.10
96.90
93.30
95.50
95.60
96.30
96.20
95.70
95.90
95.90
97.30
97.00
94.50
97.00
95.30
95.70
95.50
97.20
92.70
94.80
95.90
91.90
95.60
94.80
94.70
96.90
92.00
97.40
96.30
97.80
94.90
95.60
96.60
95.10
95.30
96.10
97.90
94.90
94.80
95.90
96.70
96.30
95.90
96.10
97.10
94.70
96.21)
96.60
96.10
96.40
95.20
94.10
95.00
96.80
97.50
97.60
96.00
95.10
93.00
93.60
92.70
94.80
95.00
95.30
93.10
97.70
91.80
91.60
96.50
96.00
95.40
94.30
93.30
95.20
9T.OO
92.90
96.00
96.20
96.40
97.70
Mean Income
Per Family
Member
IA3
3092.00
27H7.00
39H3.00
3006.00
2551.00
3133.00
2757.00
2879.00
2845.00
3022.00
3691.00
3162.00
2317.00
2805.00
3172.00
2791.00
2899.00
2683.00
2430.00
2376.00
3259.00
3397.00
2696.00
2283.00
2631.00
3047.00
2835.00
2199.00
3199.00
3329.00
2707.00
2718.00
3121.00
3241.00
2613.00
2925.00
2SS6.00
2563.00
3492.00
2748.00
3101.00
3415.00
3364.00
3376.00
2753.00
3145.00
2978.00
2704.00
3541.00
2372.00
2535.00
3627.00
3179.00
2956.00
2949.00
3196.00
2513.00
3362.00
3074.00
3274.00
3386.00
3080.00
3077.00
3359.00
32)7.00
2741.00
2518.00
3190.00
3030.00
6289.00
3063.00
3193.00
3672.00
2921.00
3187.00
29S5.00
3165.00
3453.00
3809.00
3105.00
2685.00
3397.00
3300.00
3240.00
t of
Children
Under 18
Living With
Both Parent!
IAA
82.70
80.80
86. BO
91.60
73. BO
79.00
80.30
78.10
83.20
86.90
86.10
87.50
72.50
82.00
80.70
78.80
83.30
76.60
71.80
81.80
88.00
84.50
86.20
80.90
86.40
86.60
64.20
74.90
64.50
86.90
79.10
61.30
86.40
85.00
62.30
83.60
73.30
87.60
86.10
81.20
B8.80
86.10
81.50
67.50
78.70
86.60
88.70
76.10
88.20
77.20
72.80
83.20
82.80
80.70
80.00
85.50
78.70
88.00
80.90
86.80
B6.60
65.70
78.90
82. 50
80.30
68.10
74.10
87.00
63.00
87.70
79.70
63.40
81.70
82. SO
82.30
86.70
62.00
87.20
71.00
84.30
86.80
61.50
87.40
87. SO
1 of
Harried
Couplea
Without Own
Household
IAS
1.30
1.00
1.00
.60
1.80
1.20
1.10
1.40
1.01)
1.10
1.40
1.00
1.70
1.10
1.20
1.50
.70
1.70
1.70
2.00
.60
.80
.80
2.00
1.00
.SO
1.10
1.20
1.10
.50
1.30
1.40
1.20
1.20
1.50
1.50
1.90
1.70
1.00
1.70
1.00
.70
1.30
1.30
1.10
1.40
1.40
1.70
.40
1.70
1.90
1.40
.80
1.20
1.30
1.30
1.40
.70
1.20
1.80
.70
1.00
1.20
1.00
.90
2.20
1.30
.»0
.80
1.50
1.10
.60
2.00
1.70
.60
1.30
.80
1.40
1.30
.70
2. SO
1.50
1.00
1.30
Per Capita
Local Gov't
Expend . on
Education
IB1
US. 69
152. «3
175.06
226. 68 •
105.28
132.93
21B.27
145.98
133.11
222.52
138.73
121.86
88.52
119.83
142.27
121.68
164.42
94.23
105.90
163.98
142.52
161.59
171.10
122.75
124.67
184.50
121.46
100.51
199.60
129.62
187.01
89.06
131.49
154.56
104.60
103.13
• 102.64
113.32
153.13
114.42
157.20
193.34
158.33
132.41
105.01
135.06
129.99
US. 52
171.42
93.66
89.40
148.31
144.96
123.90
126.11
231.74
14S.95
134.75
121.97
141.16
131.29
148.5?
183.43
173.92
118.34*
96.81
1S9.44
137.00
147.32
196.13
173.78
176.64
U4.20
167.66
132.99
192.35
170.44
113.54
174. »9
149.55
95.06
147.1}
136.43
123.22
I of
Persona
25+ ,
Completed
4 yti High
School or More
182
S2.30
66.20
67.50
56.20
46.30
60.90
51.70
59.10
46.30
58.90
51.90
5J.40
419.40
52.80
50.60
47.60
72.90
50.60
46.60
47.10
55.60
66.00
56.20
51.10
58.40
61.90
52.00
S5.10
52.30
S9.40
S2.70
4.1.00
47.80
!>5.40
46.10
SB. 30
',6.10
44.10
60.70
50.70
43.90
63,. 10
65.20
53.70
56.50
52.60
54.50
47.40
71.20
42.30
51.60
56.60
54.10
52.10
56.10
63.80
51.00
53.70
53.60
43.30
52.20
50.60
62.50
71.30
63.60
48.00
50.90
54.20
65.30
6H.10
49.40
60.70
52.80
63.10
SB. 20
49.90
62.90
49.90
SS.70
63.20
4i.»0
54.40
S3. 70
44.70
1 of
Male.. 16-64
Leas Than
15 yra School
But Vocational
Training
I83a
28.70
31.50
26.20
26.90
29.50
28.70
24.60
30. 80
30.40
27. BO
35.30
27.50
33.70
22.30
29. SO
25.30
36.30
26. SO
26.20
26.80
32.10
26.90
25.90
26.80
30.10
29.60
24.10
32.90
27.20
30.20
23.40
22.60
26.70
33.10
23.70
26.20
21.90
23.30
30.00
22.50
22.70
28.10
38.20
32.30
27.40
30.10
33.20
26.50
29.40
22.90
24.30
30.40
41.10
39.70
34.90
35.30
33.40
30.00
27.70
26.50
28.20
27.60
28.40
34.60
30. ?0
30.50
28.90
30.50
32.20
34.50
27.10
37.20
32.50
31.40
31.90
29.50
41.70
33.40
30.20
32. «0
30.60
32.30
33.50
29.20
% of Females
16-64
Leas Than
15 yra School
But Vocational
Training
IB3b
21.90
24.70
25.00
1U. 60
19.90
?4.60
IB. 90
?6.20
22.30
20.10
27.40
22.30
21.20
16. SO
24.70
21.20
?6.bO
19.20
16.90
20.10
22.20
21.60
19.70
20.50
19.50
22. BO
16.60
21.10
20.10
22.70
18.20
17.70
20.00
23.50
14.90
18.50
19.90
16. bO
22.60
1 7.60
16.50
22.20
?9.40
24.30
22.40
22.50
24.90
19.40
22.90
17.30
22.60
26.70
27.40
23.10
25.70
24.90
22.10
22.30
26.00
19.00
19.40
19.40
23.90
25.70
25.10
21.40
22.90
22.30
26.10
28.90
21.90
28.30
24.90
23.60
23.60
24.60
24.90
26.10
24.50
24.10
19.50
25.40
27.90
19.90
Motor Ve1
Regletra
1,000
1C)
551. 00
6b4 .00
49*».oo
472.1MI
b32.00
565. 00
f><»3.00
5H4.00
b94.00
470.00
601.00
S6S.OO
4b3.00
boo. oo
629.00
552.00
609.00
blb.OO
509. OC
S70.0C
574. OC
h?l .01
538. 0(
S12.0I
4H1 .01
61b.OI
S«b. 01
111.0
b?3.0
556.0
632.0
b7b.O
549.0
4X1.0
509.0
61 7.0
573.0
4R1 . 0
5PO.O
546. D
481.0
S1R.O
64H . 0
4M1.0
589. C
572.0
4ft 1 . r
547. t
494. (
561. C
572.1
553.1
574.1
456.1
701.1
594.1
595.1
562. 1
741.
4BI.'
54H.
517.
549.
600.
6M4.
4B1.
5X4.
530.
652.
603.
623.
573.
4B1.
656.
672.
444.
604 .
553.
704,
655,
4S1,
579,
481,
481
254
-------
TABLE B-5 (Continued)
Ul
66 ALB
67 ANN
nfl APP
69 >l>6
70 AUS
71 HAK
72 BAT
73 HIA
74 hlN
7b MKI
76 CAN
77 CHA
78 CHA
79 CHA
80 CHA
81 CCJL
Mi COL
83 COL
64 COR
85 J)A»
86 (IE 5
87 UUl-
88 ELP
89 Fftl
90 FUG
91 EV»
92 FAY
9J HI
94 fO«
9b Fl
148 VOR
Motorcycle
1.000 pop.
IClb
16.00
32.00
25.00
13.00
8.00
23.00
43.00
18.00*
14.00
8.00
10.00*
12.00
12.00
33.00
17.00*
15.00
28.00
8.00
17.00
13.00
20.00
31.00
26.00
16.00
12.00
36.00
19.00
17.00*
26.00
16.00
29.00
7.00
15.00
12.00
18.00
21.00
16.00*
12.00
24.00
16.00
12.00
29.00
36.00
10.00*
13.00
15.00
10.00*
16.00
16.00
14.00
14.00
10.00*
10.00*
17.00*
19.00
3b.OO
17.00
17.00
17.00*
12.00
13.00
24.00
25.00
34.00
34.00
12.00
18.00*
15.00
24.00
10.00 •
27.00
19.00
10.00*
24.00
29.00
7.00
32.00
10.00*
24.00
34.00
12.00
17.00*
10.00 •
12.00
t of
Households
With One
Automobiles
IClc
82.50
89.70
91.20
89.70
82.30
90.40
89.20
88.10
86.10
85.40
H5.20
88.00
80.10
80.30
84,90
82.70
92.60
65.20
82.10
88.50
87.80
86.90
81.40
84.20
86.10
91.10
84.40
87.40
91.00
86.90
88.20
85.80
88.70
85.00
79.00
89.40
83.80
82.20
90.90
84.90
84.70
91.40
92.80
81.10
84.60
91,20
82.50
ni. 90
85.90
63.10
79.70
81.90
89.00
85.50
88.40
93.80
86.40
86. 50
87. ID
82.80
88.90
89.30
90.40
90.40
89.40
78,90
81.90
87.60
85.30
89.50
85.80
88.40
82.60
90.00
88.40
83.60
90.70
84.40
87.50
90.80
79.80
87.20
82.30
87.90
Local Sunday
Newspaper
Clrc . /
1,000 pop.
IC2a
243.00
376.00
363.00
844.00
1133.00
341.00
703.00
540.00
666.00
1266.00
565.00
778.00
1373.00
1488.00
852.00
1021.00
602.00
1067.00
390.00
425.00
835.00
7421.00
820.00
267.00
752.00
713.00
611.00
789.00
583.00
604.00
856.00
1631.00
453.00
2487.00
762.00
385.00
722.00
1377.00
773.00
936.00
2043.00
611.00
802.00
712.00
1705.00
501.00
454.00
604.00
642.00
501.00
595.00
940.00
1228.00
584.00
1655.00
311.00
1075.00
898.00
1268.00
1048.00
567.00
664.00
372.00
639.00
1069.00
944.00
648.00
1014.00
752.00
292.00
464.00
663.00
1046.00
333.00
541.00
634.00
412.00
539.00
1294.00
643.00
947.00
1156.00
610.00
901,00
X Occupied
With TV
IC2b
95.50
95.00
95.00
98.50
94.60
94.30
94.90
96.50
95.90
96.60
97.50
97.10
94.60
95.40
95.90
96.10
95.50
95.40
95.30
94.30
97.00
96.70
96.00
95.60
97.50
93.70
97.50
95.90
97.50
96.80
95.00
95.90
97.20
96.50
95.90
96.80
95.00
96.70
97.30
95.60
69.30
96.50
96.10
97.30
95.90
97.70
97.90
95.50
95.00
94.20
95.20
96.70
96.40
95.90
95.60
96.60
94.70
96.00
95.80
96.50
96.50
97.90
93.80
93.30
93.70
98.10
95.20
97.30
95.50
98.00
94.00
95.40
97.00
94.60
95.70
97.20
95.80
97.60
95.10
95.60
97.60
97.40
97.80
96.00
Local Radio
1,000 pop.
Ic2c
.03
4.43
1.28
.72
3.»5
3.71
3.34
3.15
1.S6
1.32
1.54
.80
1.97
3.47
2.20
2.95
5.93
2.47
3.34
2.10
1.37
2.44
3.01
2.78
1.13
4.69
1.71
1.41
1.20
2.50
2.90
2.33
1.32
1.45
1.96
2.63
1.93
1.52
2.97
2.75
1.56
2.11
3.29
.86
2.16
.38
1.87
2.42
2.41
2.65
4.47
1.12
.48
.34
2.33
.53
3.29
1.16
3.07
1.68
1.83
1.81
1.60
3.40
1.46
2.13
3.05
1.78
5.57
.48
2.41
2.18
.98
3.69
1.67
2.05
0.00
1.43
.85
2.82
1.75
1.20
2.03
1.21
Population
Density
In SKSA
IC3a
360.00
270,00
329.00
197.00
180.00
292.00
40.00
621.00
241.00
146.00
2016.00
646.00
146.00
253.00
350.00
306.00
109.00
220.00
217.00
187.00
213.00
495.00
36.00
340.00
324.00
47.00
219.00
324.00
382.00
418.00
69.00
233.00
480.00
253.00
180.00
169.00
157.00
148.00
3S9.00
282.00
338.00
222.00
35.00
1119.00
217.00
519.00
1397.00
325.00
242.00
134.00
142.00
1450.00
562.00
1150.00
3&2.00
202.00
143.00
190.00
266.00
344.00
339.00
270.00
75.00
97.00
128.00
516.00
169.00
308.00
164.00
1702.00
206.00
245.00
1333.00
38.00
126.00
128.00
155.00
968.00
172.00
159.00
366.00
4Z9. «0
727. •»
230.00
»BM.
Pop.
Under 1
end 6S*
In Central
City
IC3b
18.30
15.20
13.40
18.00
19.60
15.60
17.60
15.60
17.40
73.10
20.60
21.40
17.80
18.90
16.30
19.40
16.80
13.40
15.60
15.70
20.00
19.70
21.00
16.10
20.10
15.80
19.80
15.10
19.40
19.30
16.90
17.90
19.40
22.80
70.50
14.10
16.90
70.90
17.70
17.70
21.40
18.90
15.00
23.30
19.00
17.80
21.10
17.70
15.30
17.40
17.80
20.70
20.50
14.30
70.40
15.50
17.60
19.40
14.90
23.10
19.90
19.60
17.10
24.70
22.20
21.30
19.10
H. «0
21.60
17.40
20.00
20.50
20.80
18.60
17.50
23.10
17.10
21.10
24. to
17. .1
21.30
IJ.3»
•*.*••
23.30
Negro to
Median Total Pop.
Family Professionsl
Income Adj. Em>. Ad).
For Educetlon For
mi
.78
.74
.95
1.00 •
.86
.72
.67
.73
.77
.97
.79
.91
.85
.77
.74
.74
.73
.84
.68
.72
.76
.71
.82
.83
.87
.80
.76
.85
.90
.90
.73
.82
.87
.76
.80
.70
.76
.83
.85
.76
.76
.84
.72
.77
.70
.91
.49
.79
.66
.75
.72
.73
.84
.86
.'2
.74
.75
.81
.72
.76
.85
.88
.74
.67
.66
.90
.76
.67
.77
.57
.73
.79
.79
.78
.61
.86
.78
.78
.83
.72
.62
.71
• 75
.73
Education
IIA2
.07
.01
.04
1.00*
.20
.05
.08
.20
.11
.01
.03
.02
.23
.04
.13
.10
.02
.16
.26
.03
.0?
.03
.01
.03
.02
.01
.03
.22
.06
.03
.03
.08
.03
.03
.02
.OH
.24
.01
.03
.04
.01
.03
.03
.01
.11
.03
.01
.16
.01
.20
.26
.06
.02
.16
.06
.01
.09
.02
.11
.02
.02
.06
.02
.01
.01
.01
.21
.04
.01
.03
.04
.02
.08
.01
.04
.01
.04
.03
.11
.04
.01
.06
.01
.02
Negro Halts
Males
Unemployment
Rate Adj.
For Educetlon
IIA3
2. On
2.12
1.77
1.00 •
2.35
1.67
3.17
2.63
3.1?
2.7S
1.81
3.8A
2.8")
1.2V
2.?7
2.6?
1.88
2.57
2.16
1.31
2.7S
2.80
1.67
1.66
4.0V
1.43
4.36
1.5?
1 . M I
2.9S
3.54
1.H4
?.!>
2.8?
1.00
?. V*
2.74
1.7«
3.60
?.4«
4.8?
2.2*
1.13
.37
2.71
2.14
1.03
2.9?
2.37
2.13
2.71
1.96
1.25
7. on
7.04
1 .9b
2.97
3.61
2-flf.
5.4?
2.50
1.5?
1.10
2.0v
1.35
2.4V
2.7V
3.65
1.64
2.73
2.12
1.42
2.75
1.82
2.20
Z.27
1.55
2.04
2.15
1.61
4.57
3.03
2.77
3.03
255
-------
TABLE B-5 (Continued)
us
66 ALB
67 ANN
60 APP
69 AUG
70 AUS
71 HAK
72 bAI
73 BEA
74 HIN
7S HHI
76 CAN
77 CHA
78 CMA
79 CMA
80 CHA
81 COL
82 COL
83 COL
8* COR
05 OAW
86 OES
87 OUL
88 FLP
89 EHI
90 FUG
91 EVA
92 FAY
93 FL1
94 FOR
95 FOE
46 GRF
97 HAM
98 HAR
99 HUN
100 HUN
101 JAC
102 JOH
103 KAL
104 KMO
lOb LAN
106 LtN
107 LAS
108 LA»
109 LIT
110 LOR
111 LOM
112 MAC
113 MAD
11« MOB
115 MON
116 NEW
117 Nt«
118 NEK
119 ORL
120 OXN
121 PEN
122 PEO
123 PAL
124 UFA
125 RUC
126 SAG
127 S«L
128>SAN
129 SAN
130 SCR
131 SCH
132 SOU
133 SPO
134 SIA
135 SIO
136 TAC
137 TWE
138 TUC
139 TUL
140 UTI
141 VAL
142 VAT
)43 VES
144 «IC
145 »IL
146 MIL
147 HOP.
14B TOR
Negro Female*
To Total
Peualei
Unemployment
Rate Adj.
For Education
IIA4
1.79
.74
1.96
1.00 •
2.05
1.44
1.92
2.18
2.08
1.06
1.39
2.43
2.01
1.49
2.16
1.62
2.11
1.90
1.90
.76
2.52
1.94
.84
1.65
1.60
1.00*
2.24
1.46
1.94
1.98
1.41
1.69
2.35
2.51
1.87
2.00
2.39
3.85
2.02
1.83
3.07
2.02
1.18
1.31
2.05
1.34
1.00 •
1.90
.87
1.83
2.08
2.25
1.S8
1.64
1.78
1.37
1.87
1.84
2.53
1.05
2.19
2.26
1.52
1.56
.75
1.00*
2.33
1.85
1.39
1.41
2.22
1.32
1.57
.86
1.91
2.61
1.36
1.56
2.31
1.75
2.37
1.64
1,18
1.65
Hale to
female
Unemployment
Rate Adj.
for Education
IIB1
,75
.72
1.12
.55
.53
.70
.75
.75
.«8
.77
.80
.71
.48
.89
.46
.57
.65
.50
.49
.65
.70
.89
.92
.80
.39
.86
.56
.47
.54
.82
.74
.46
.54
.70
.93
.62
.63
1.14
.80
.63
.38
1.06
.67
.98
.70
.60
.93
.44
1.18
.62
.58
.84
.64
.49
.67
.75
.51
.51
.51
.67
.61
.71
.68
.84
.95
1.47
.65
.65
.88
.98
.76
1.00
.76
.71
.58
.76
.67
.59
.91
.85
1.43
.62
.97
.52
Male to
Female
Profeaalonal
Enp. Adj.
For Education
IIB2
1.49
1.91
1.47
1.43
1.14
1.63
1.72
1.47
1.42
1.98
1.83
1.45
.96
1.63
1.26
1.23
1.32
1.20
.71
1.25
1.52
1.26
1.29
1.26
1.53
1.41
1.37
.72
1.39
1.76
1.34
1.25
1.S8
1.66
1.25
2.65
1.19
1.20
1.40
1.58
1.52
1.59
1.60
1.65
1.19
1.59
1.79
1.10
1.56
1.30
1.01
1.53
1.56
1.64
1.54
2.10
1.23
1.52
1.72
1.44
1.63
1.42
1.21
1.66
1.21
1.40
1.09
1.81
1.29
1.85
1.34
1.28
1.83
1.59
1.95
1.62
1.20
1.34
1.55
1.49
1.31
2.02
1.36
1.61
X Working
Outelde
County of
Realdence
I1C1
17.80
2. BO
12.10
15.80
14.20
2.90
3.70
9.60
10.00
13.00
12.00
11.30
11.60
4.60
7.90
12.50
1.80
18.80
34.20
7.90
16.60
2.90
7.40
4.00
2.80
4.10
12.40
2.40
7.60
3.00
4.10
9.40
22.50
24.00
24.10
9.20
11.90
14.10
6. SO
12.80
9.80
23.20
4.60
21.40
6.30
18.00
14.10
13.90
3.40
10.00
10.60
5.90
8.00
34.10
14.70
18.30
9.70
26.00
7.90
14.20
9.50
8.30
3.50
3.50
15.50
9.10
15.70
12.20
2.40
21.30
5.60
9.40
14.00
3.00
8.20
10.10
19.40
16.90
4.20
5.00
11.50
14.60
8.30
15.40
Central
City 4
Suburban
Income
Dl.t.
I1C2
.06
-.12
-.08
-.04
.0*
0.00
-.06
-.08
0.08
-.02
.10
.04
-.06
-.12
-.02
.08
-.04
0.00
-.10
-.06
-.04
0.00
-.06
-.04
.02
-.06
-.02
-.08
0.00
.04
-.04
-.04
-.02
.04
-.10
-.18
-.14
0.00
.04
0.00
.04
0.00
-.02
.04
-.14
.02
.04
0.00
-.02
-.12
-.12
.10
0.00
.06
0.00
-.02
-.04
-.04
-.10
.06
-.04
.04
-.06
-.06
-.08
-.02
-.12
-.04
-.02
.26
-.02
0.00
.18
.09
-.12
.02
-.08
.06
.04
-.06
0.00
.04
0.00
.04
I Famlliea
Routing With Income
Segregation Above Poverty
Index Level
IIC3 IIIAI
.27
.08
.09
.86
.75
.10
1.22
.04
.53
2.16
1.13
1.15
.43
.72
.32
1.39
.02
.15
.07
.28
.47
.30
.87
.22
.82
1.29
.29
.54
1.27
.57
.99
1.08
.76
3.53
.65
.24
.07
3.70
1.76
.80
3.42
1.29
.21
1.05
.21
.57
.57
.29
.13
.19
.05
1.21
1.03
.05
1.02
1.21
.93
1.51
.01
2.36
.39
.95
.71
.26
.39
1.29
.03
1.10
.11
.64
.98
.47
1.26
.20
.29
1.14
.49
.76
.40
.35
1.14
2.64
.46
3.96
89.30
87.00
94.90
94.50
84.60
89.20
87.40
86.40
88.40
92.70
94.80
94.20
79.40
87.00
90.10
86.70
90.80
85.70
81.30
81.60
93.30
93.90
91.70
82.60
93.20
92.10
90.90
82.90
93.10
94.90
85.80
88.20
93.00
93.40
85.20
66.50
81.40
90.30
94.20
85.70
93.50
93.90
93.00
94.20
86.60
9*. 30
93.90
84.90
84.60
81.40
80.80
92.70
91.70
89.90
88.70
92.60
84.50
94.30
88.80
95.00
93.60
92.30
90.40
92.40
89.60
92.20
81.80
94.10
91.40
96.00
88.80
92.00
93.60
H9.20
90.20
92.60
91.20
94.50
89.80
92.00
91.10
92.90
94.60
94.10
94.50
97.50
97.90
96.30
93.10
97.50
98.60
97.50
96.90
97.30
97.40
97.40
88.60
93.70
96.10
95.90
98.40
91.70
93.30
93.10
96.10
96.80
91.70
92.10
97.50
98.40
95.00
92.50
97.60
97.90
98.30
92.20
94.50
95.60
90.10
91.70
92.10
91.60
97.40
92.50
94.60
97.00
99.20
96.80
95.80
97.50
96.90
93.40
96.10
93.00
89.50
98.30
97.50
97.70
96.50
99.40
93.10
96.70
93.20
95.50
97.30
96.90
98.80
99.00
98.90
95.90
91.20
97.50
97.20
98.50
97.40
98.30
98.10
97.50
97.10
96.00
99.20
98.10
95.60
98.10
95.10
97.30
96.90
93.90
X Occupied
Hou,Ing With
1.01 or More
Peraons
Per Room
IIIA3
8.20
10.30
5.90
7.90
10.40
8.90
10.90
10.80
9.60
5.10
6.20
5.90
11.40
7.10
7.80
8.30
5.90
9.00
11.40
16.70
6.70
6.00
7.40
18.30
5.90
S.SO
8.71)
10.80
8.70
5.90
10.80
8.30
8.50
4.00
7.80
8.30
12.90
6.50
5.40
7.30
4.30
6.20
8.90
5.70
8.50
8.50
6.10
11.00
6.40
12.00
11 .00
5.40
5.50
7.20
7.10
b.40
9.30
6.50
7.90
3.70
7.10
8.80
10.20
6.70
6.50
4.70
11.40
6.20
5.30
4.80
9.30
5.20
4.90
10.50
5.80
5.20
6.70
7.60
8.80
6.90
4.40
S.10
5.60
4.70
X Occupied
Housing With
Telephone
I11A4
87.30
85.60
95.00
95.50
81.30
88.10
85.70
90.50
88.30
92.00
92.70
93.60
79.80
S5.40
85. 80
02. 90
90.20
H4.30
no.ao
79.40
43.40
93.00
93.10
HO.10
90.30
91.00
B9.3U
75.10
10.70
91.10
88.00
hi .30
SO.80
9?.00
S2.70
S3.9U
HO.50
89.70
94.50
H4.60
90.90
93.70
81.80
91.20
S5.70
90.80
92.90
H3.90
95.80
82.30
"2.90
93.90
93.70
H7.70
83.50
91.20
H3.20
92.50
H7.10
93.30
90.60
92.20
07.10
91.00
89.40
93.00
63.60
92.70
91.80
96.40
R7.70
90.00
90.80
84 .60
88.70
91.60
91.30
94.60
K2.60
90.30
91.40
91.60
94.20
90.90
256
-------
TABLE B-5 (Continued)
us
66 »LH
67 ANN
6H APP
69 AUG
70 AUS
71 HAH
If dAI
73 HI A
7*i HI N
7S hkl
76 CA«<
77 CHI
7rt C*a
79 CHI
MO CMS
HI CO.
H2 C0_
83 CO.
M4 C0°<
H5 DArf
86 111 S
87 HU.
88 F. LJ
HV F F< |
V 0 t U i
VI t V (
92 FA r
93 FL 1
94 F UK
95 FM
96 dKI
97 HAM
98 HA>-(
99 HUN
100 HUN
101 JAi:
102 JOH
103 KAI
104 ^N()
105 LAN
106 LAM
107 LA'.
108 LAiii
109 LIT
110 LOF
111 LOW
112 «A(
113 MAI
114 KOF
lib HOI-
116 Ntk
117 Ntk
1 In MFk
119 OWL
12U OXt,
121 PtN
122 PEO
123 UAL
124 KF.J
125 KOC
126 SAG
127 SAL
128 SAN
129 SAM
130 SCF)
131 SCM
132 SOU
133 SPO
134 STA
135 STO
136 IAC
137 !FS
f>.ll
4.VW
6. 04
S.OJ
4.U
5.01
S.?7
4, It
S.h^
S.Sv
4.4«
7.4-1
5. SI
6.1S,
4 ,5>-
1.6h
b.OJ
4.70
4. HI
4.31
S.99
4.37
1,h»
S.90
4. HI
4.H7
6.24
S.hh
6.?/
S.7H
3.91
5.6 1
6. S3
6.91
6. 1C
5.4H
6.01
6.14
7.53
5.87
4.42
S.H»
S.7H
7.60
5.89
S.OJ
4.76
7.43
7.11
6.09
3.93
5.28
5.89
257
-------
TABLE B-5 (Concluded)
us
66 AIR
67 ANN
68 APP
69 AUG
70 AUS
71 HAK
7? HAT
73 Bf A
74 HIM
75 HRI
76 CAN
77 CHA
7H CHA
79 CHA
80 CHA
81 COL
82 COL
83 COL
8* COR
85 DAV
86 LIES
87 IIUL
8» (LP
at (HI
90 (Ufa
91 tVA
92 FAY
93 fLI
94 FOR
95 FHE
96 GRE
97 HAM
98 MAR
99 HUN
100 HUN
101 JAC
102 JOH
103 KAL
104 HHO
lOb LAN
106 LAN
107 LAS
JOB LAD
109 LIT
110 LOH
111 LOU
112 MAC
113 HAD
114 MOB
115 MON
1th NEH
117 Nt«
1 1H NtW
119 DHL
120 OXN
121 PEN
122 MfO
123 BAL
124 REA
125 HOC
126 SAG
127 bAL
I2B SAN
129 SAN
130 SCR
131 SCH
132 SOU
133 SPO
134 STA
135 STO
136 TAC
137 TRE
13B IUC
13* TW.
140 UTI
141 VAL
142 HAT
43 »ES
44 trie
45 «IL
46 MIL
47 DOR
48 YOR
Hospital .
Bed i/
100,000
pop.
""i
486.00
363.60
772.30
535.60
464.40
312.30
342.10
425,40
434.30
462.20
324.30*
401.10
400.20
619.10
443.10
435.50
307.70
409.40
347.00
441.30
488.90
517.60
764.70
419.70
397.50
296.20
634.10
183.00
382.80
519.50
393.40
331.90
523.90
463.90
593.50
416.20
493.20
552.50
368.10
535. BO
321.90
34ft. 00
299.70
466.80 •
556.10
358.60
466.80 •
374.10
624.20
382.30
457.00
377.10 •
244.00 •
395.00
446.80
341.10
396.20
606.20
394.00
380.60
402/50
364.50
308.70
452.10
328.50
553.60
641.00
322.80
468.20
324.30 •
342.90
280.80
525.10
356.90
415.80
399.40
388.60
377.10 •
393.40
527.50
469.50
340.70
474.50*
253.40
Voll. of
Book> In
Halo
Public
Ltbr.ry/
1 ,000 pop .
IIIB6
1568.40
953.50
653.00
429.00
903.60
961.20
1513.80
834.50
291.50
773.30
1145.50
1009.90
754.10
1244.20
1236.70
679.40
832.60
733.10
1276.80
903.00
552.50
1309.00
632.50
960.60
950.50
569. BO
1841.40
404.20
689.40
4882.20
1565.30
634.80
897. 10
54B.30
537.30
582.70
H69.20
248.60
992.60
1027.00
559.70
606.60
2=>7.60
824.20
597.10
808.70
1278.60
1550.00
1071.20
600.80
790.50
1266,50
276.00
429.10
743.40
236.20
520.00
1085.80
828.30
714.00
886.00
1300.70
374.50
897.70
1123,80
752.60
738.50
928.30
1356.50
1302.60
2141.50
1158.50
750.20
1005. BO
1127.50
323.80
479.90
752.50
143.30
742.20
380.20
793.00
1927.10
331.90
Death
R«t«/
1,000 pop.
IIIC1
9.50
6.30
5.90
7.50
8.60
6.50
8.30
7.30
8.20
9.50
8.70*
9.90
7.50
10.20
7.80
9.70
5.70
7.30
7.90
6.90
9.60
9.10
11.40
6.20
10.20
6.90
10.60
5.20
7.50
B.10
8.60
8.60
8.10
9.90
10.40
6.30
«.50
11.60
7.30
8.90
B.BO
7.00
6.70
8.10*
9.30
B. 20
8.10*
9.00
6.90
a. 60
10.70
9.40*
8.20*
6.50
8.60
6.10
7.30
9.10
7.30
11.00
a. lo
8.30
6.90
7.50
10.70
13.50
9.70
9.40
10.40
6.70*
9.40
B.60
9.60
8.40
9.00
10.80
8.00
9.40 •
11.60
8.00
12.90
8. BO
10.30 •
9.60
Birth
Rite/
1,000 pop.
II1C2
17.50
18.90
19.10
18.00
20.40
18.90
17.90
21.80
16.70
18.20
15.90*
17.10
22.30
16.70
19.80
18.10
18.90
17.90
21.20
21.40
18.40
18.20
15.40
28.90
18.80
16.20
17.10
23.40
20.20
20.00
18.30
18.40
17.50
15.60
16.90
20.60
20.50
14.40
17.60
15.60
17.10
19.70
20.70
16.70*
19.20
19.20
16.70*
19.20
18.00
18.90
19.10
16.60 •
16.70 •
20.30
17.20
18.80
20.50
17.80
18.10
13.90
19.80
20.60
18.60
17.70
16.00
13.60
20.90
16.70
16.00
15.90 •
17.30
18.90
16.70
16.90
17.00
17.50
18.10
16.80 •
14.70
19.50
13.60
16.00
15.80 •
16.90
Sports
Events
IIIC3
N.A.
0.00 •
12.00
12.00
2.00
4.00
0.00 •
4.00
3.00
5.00
7.00
3.00
4.00
3.00
10.00
0.00*
5.00
5.00
5.00
0.00
5.00
6.00
9.00
7.00
10.00
5.00
5.00
0.00*
10.00
9.00
8.00
6.00
2.00
4.00
3.00
0.00
3.00
6.00
4.00
4.00
4.00
5.00
6.00
3.00
5.00
0.00
0.00 •
0.00 •
5.00
2.00
0.00 •
14.00
0.00 •
11.00
0.00*
0.00
1.00
4.00
7.00
4.00
0.00 •
2.00
2.00
0.00 •
0.00 •
8.00
4.00
9.00
6.00
0.00
6.00
5.00
5.00
0.00*
9.00
0.00 •
0.00
2.00
0.00*
4.00
5.00
6.00
5.01
3.00
Dance
Drama
and
Music
Events
IllMa
N A.
i. 00*
51.00
K4.0Q
33.01]
45.00
2.00*
70.00
32.00
32.00
16.00
17.00
30.00
12.00
35.00
2.00 •
14.00
43.00
27.00
21.00
18.00
35.00
38.00
39.00
42.00
2H.GO
24.00
2.00*
50.00
12.00
51.00
30.00
4.00
29.00
39.00
30.00
39.00
20.00
46.00
71.00
32.00
27.00
27.00
20.00
55.00
2.00
2.00*
2.00*
77,00
24.00
2.00 •
40.00
2.00*
49.00
2.00*
2B.OO
2B.OO
54,00
59, 00
26.00
2.00*
40,00
11.00
2.00*
2.00 «
52,00
J2.00
B2.00
11 .00
32.00
19.00
45.00
20.00
2.00*
55.00
2.00*
9.00
7.00
2.00 •
33.00
79.00
31.00
35.00
16.00
Cultural
Institutions
IlIC4b
N.A.
0.00*
4.00
3.00
2.00
0.00
0.00 •
14.00
4.00
H.OO
3.00
2.00
2.00
2.00
10.00
o.oo •
3.00
4.00
2.00
2.00
2.00
3.00
3.00
4.00
1.00
4.00
1.00
0.00 •
6.00
2.00
3.00
2.00
1.00
7.00
3.00
2.00
3.00
4.00
2.00
h.OO
4.00
3.00
7.00
3.00
7.00
0.00
0.00*
0.00 •
4.00
1.00
0.00.
5.00
0.00*
10.00
0.00 •
0.00
6.00
11.00
4.00
1.00
0.00*
2.00
1.00
0.00*
0.00 •
1.00
b.OO
11.00
3.00
1.00
2.00
4.00
5.00
0.00*
4.00
0.00 •
1.00
1.00
0.00 •
4.00
1.00
6.00
5.00
1.00
Fain
and
Festivals
Held
IIIC4C
N.A.
0.00 •
6.00
6.00
2.00
6.00
0.00*
IB. 00
6.00
3.00
3.00
3.00
7.00
2.00
6.00
0.00 •
0.00
6.00
6.00
12.00
4.00
7.00
6.00
8.00
15.00
2.00
5.00
0.00 •
B.OO
3.00
6.00
10.00
4.00
9.00
2.00
l.no
6.00
5.00
5.00
4.00
3.00
4.00
8.00
4.00
6.00
2.00
o.oo •
o.oo •
3,00
6,00
0,00*
5.00
0.00 •
7.00
0.00 •
2.00
3.00
9.00
2.00
4.00
0.00 •
2.00
5.00
0.00 •
0.00 •
6.00
4.00
6.00
3.00
16.00
5.00
7.00
2.00
0.00*
15.00
0.00*
4.00
3.00
0.00 •
3.00
10.00
2.00
5.00
2.00
258
-------
LIST C
SMSA'A WITH POPULATION LESS THAN 200.000 (S)
SMSA
141
150
ni
1S2
m
1 Si
1S5
Sti
157
Ii8
\ >2
1 )3
l')4
l'.5
lr,6
111?
In8
K-9
0
1
2
73
74
175
176
177
179
IfO
181
182
183
184
185
186
187
188
189
19)
191
19!
19i
19n
19 i
19ii
19'
19«
Abilene, Texas
Albany, Ca .
Altoona, Pa.
Amarillo, Texas
Anderson. 7nd.
Abbeville, N C
Atlantic City. N J.
Bay City, Mich
Billings, Mont
Bl loxl-ful fport, Miss.
Bloomington-Normal , 111.
Bcnse City, Idaho
Bristol, Conn.
Brockton, Mass.
Brownsville-Harlingen-San Benlto, Texas
Brvan-College Station, Texas
Cedar Rapids, Iowa
Champaign-Urbana, 111
Columbia, Mo.
Danburv, Conn.
Decatur, 111.
Dubuque, Iowa
Durham, N.C
Fall River, «ass.-R I
Fargo-Moorhead, N. Dak -Minn.
Fi tchburg-Leomtnster , Mass.
Fort Smith, Ark.-Okla.
Gadsden, Alabama
Gainesville, Fla
Great Falls, Mont.
Green Bay, wis
Jackson. Mich.
Kenosha, Wis
La Crosse, Wis.
Lafayette, La.
Lafayette-West Lafayette, Ind.
Lake Charles, La.
Laredo, Texas
Lawton, Ok la.
Lewiston-Anburn, Maine
Lexington, Ky .
Lima, Ohio
Lincoln, Nebraska
Lubbock, Texas
Lynchburg, Va.
Manchester, N.H
Mansfield, Ohio
McAllen-Pharr-Edinburg, Texas
Meriden, Conn
ABt
ALB
ALT
AHA
AND
ASH
ATL
BAY
BIL
B1L
BLO
BOI
BRI
BRO
BRO
BRY
CED
CHA
COL
DAN
DEC
PUB
DUR
FAL
FAR
FIT
FOR
CAD
GAI
GAL
ORE
GRE
JAC
KEN
LAC
LAF
LAF
LAK
LAR
LAW
LEW
LEX
LIM
LIN
UJB
LYN
MAN
MAN
MCA
HER
Population, 1970
fin 1.000)
114
90
135
144
138
145
175
117
87
135
104
112
66
190
140
58
163
163
81
79
125
91
190
150
97
160
94
105
170
82
158
143
118
80
110
109
145
73
108
73
174
171
168
179
123
10F
130
182
56
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
SMS*
Mldlsnd, T.xn
Modesto, Calif.
Monroe, La.
Muncle, Ind.
Huskegon-Huskegon Heights, Mich.
Nashua, H.H
New Bedford, Muss.
New Britain, Conrt.
Norvalk, Conn.
Odessa, Texas
Ogden, Utah
Owensboro, Ky.
Petersburg-Colonial Heights, Va .
Pine Bluff, Ark.
Plttsfield, Mass.
Portland, Maine
Provo-Orem, Utah
Pueblo, Colo.
Racine, Uls.
Reno , Nev .
Roanoke, Va.
Rochester, Minn.
St. Joseph, Mo.
Salem, Dreg
Savannah, Ga.
Shennan-Denlson, Texas
Sioux City, lova-Nebraska
Sioux Falls, S. Dak.
Springfield, 111.
Springfield, Mo.
Springfield, Ohio.
Steubenville-Welrton, Ohio-W Va.
Tallahassee, Fla.
Terre Haute, Ind.
Topeka, Kans
Tuscaloosa, Alabama
Tyler, Texas
Vineland-Millville-Bridgeton, N.J.
Waco, Texas
Wheeling, V Va.-Ohio
Wichita. Falls, Texas
Wilmington, N.C.
Cod«
MID
MOD
MON
MUN
MUS
DAS
NEW
NEW
NOR
ODE
OGD
OWE
PET
PIN
PIT
POR
PRO
PUE
RAC
REN
ROA
ROC
STJ
SAL
SAN
SAV
SHE
SIO
SIO
SPR
SPR
SPR
STE
TAL
TER
TEX
TOP
TUS
TYL
VI N
WAC
WAT
VHE
WIC
WIL
Population, 1970
(In 1.000)
65
195
115
129
157
67
153
145
120
92
126
'9
129
85
80
142
138
116
171
121
87
187
71
188
83
116
95
161
153
156
166
103
175
101
155
116
97
121
147
133
183
126
107
259
-------
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CHART 1
DATA SOURCES - ECONOMIC COMPONENT
Factor
IA
IB1
IB2
IBS
IB4
IBS
IIA
IIB
IIC1
IIC2
IIC3
IIC4
IIC5
I ID
IIE1
IIE2
IIF
IIG
Sources
COP. T. 89 and COP. US_, T. 105
C&C, Item 120
U.S. Department of Commerce, Survey of
Current Business, May 1974, Part II,
Tables 1 and 2
C&C, Item 87
C&C, Item 101
C&C. Item 88
COP, US_, Tables 141 and 184
C&C, Items 39 and 41
C&C, Item 129
COP, T. 87; SA, 1971, T. 1098; U.S.
Department of Commerce, Construction
Reports - Housing Authorized by Building
Permits and Public Contracts, 1970
C&C, Items 135 and 148
C&C, Items 160 and 162
C&C, Items 151 and 158
C&C, Item 118
COP, Tables 81 and 89 and COP, US, Tables
107 and 116
COP, US., Tables 141 and 184
C&C, Item 37
MRI Questionnaire
Year
1969
1970
1972
1970
1970
1970
1969
1970
1967
1970
1967
1967
1967
1970
1969
1969
1970
1970
284
-------
CHART 2
DATA SOURCES - POLITICAL COMPONENT
Factor Sources
IA1 Ayer Directory of Newspapers and Periodicals
IA2 U.S..Department of Commerce, Census of
Housing, Housing Characteristics for States,
Cities, and Counties. Table 41
IA3 The Working Press of the Nation, Vol III, 1974
Edition and SA, 1972, T. 801
IB C&C, Item 102; SAT 1973, Section 33; COP, T. 24
IIA1 COG, Vol 5, Tables 5 and 8; COG. State Parts,
T. 13
IIA2 Same as IIAl
I1A3 International City Management Association,
Municipal Yearbook (Washington, D.C., 1971),
Tables C 4/6 and C 4/11
IIA4 International City Management Association,
Municipal Yearbook (Washington, D.C., 1971),
Tables C 4/7 and C 4/12
IIA5 International City Management Association,
Municipal Yearbook (Washington, D.C., 1971),
Tables E 1/2 and E 1/7
IIA6 Same as IIA5
IIA7 Same as IIA5
IIA8 U.S. Department of Labor, Manpower Report of
the President, 1972, Tables D6 and D10
IIBl U.S'. Federal Bureau of Investigation, Uniform
Crime Reports for the United States, 1972
IIB2 Same as IIBl
IIB3 COG. Vol 5, Tables 9 and 12; COG. State Parts,
T.18; SA, 1971, T.12
IIB4 Same as IIB3
Year
1971
1970
1974
1968 and 1972
1967
1971
1971
1971
1971
1971
1970
1972
1972
1967
1967
IICl
IIC2
IIC3
Same as IIB3
C&C, Item 70
C&C, Item 76
1967
1971
1972
285
-------
CHART 3
DATA SOURCES - ENVIRONMENTAL COMPONENT
Factor Sources Year
IA1 Air Quality Data - 1972 Annual Statistics 1972
IA2 Same as IA1 1970
IB1 Same as IA1, Figure D-l 1973
IB2 U.S. Department of Commerce, Census of 1970
Housing, Plumbing Facilities and Estimates
of Dilapidated Housing
IB3 Bureau of Outdoor Recreation, Public Outdoor 1972
Recreation Acres and Facilities Inventory
IC1 COP, US, T.35 1970
IC2 U.S. Department of Transportation, Federal 1971
Highway Administration, Motor Vehicle
Registration by Standard Metropolitan
Statistical Areas-1971 and j3A, 1972, Table 889
IC3 Same as IC2 1971
ID Brian J.,L. Berry, et.al; Land Use, Urban Form 1970
and Environmental Quality (The University of
Chicago; Department of Geography Research
Paper No. 155, 1974), page 268; COP, Table 87;
C&C, Item 129
IE The Mitre Corporation, The PDI Index (Working 1971
Paper 7963) Table IV, September 1971
IIA1 See IBl 1973
IIA2 C&C, Item 493 1970
IIA3 U.S. Department of Commerce, Local Clima- 1973
tological Data
IIA4 Same as IIA3 1973
IIA5 Same as IIA3 1973
IIBl Same as IB3 1972
IIB2 Same as IB3 1972
286
-------
CHART 4
DATA SOURCES - HEALTH AND EDUCATION COMPONENT
Factor Sources Year
IA1 U.S. Department of Health Education and 1968
Welfare, Vital Statistics of the U.S.,
1968, Vol I, Tables 1-53 and 2-1 and Vol II,
Part B, Tables 7-1 and 7-4
IA2 C&C. Item 22 1969
IB1 COP. US, Tables 140 and 183 1970
IB2 Same as IBl 1970
IB3 COP, T.83 and COP, US, T.99 1970
IB4 Same as IB3 1970
ILA1 SA, 1972, Section 33 1970
IIA2 SA, 1972, Section 33 and Hospitals; A 1969 and 1970
County and Metropolitan Area Data Book
IIA3 Hospitals; A County and Metropolitan Area 1969
Data Book
IIA4 SA, 1972, Section 33 1971
IIA5 COG, Vol 5, Tables 9 and 12; COG, State 1967
Parts, T. 18; SA, 1971, T. 12
IIB1 Same as IIA5 1967
IIB2 C&C, Item 27 1970
287
-------
CHART 5
DATA SOURCES - SOCIAL COMPONENT
Factor
IA1
IA2
IA3
IA4
IA5
IB1
IB2
IB3a
IB3b
ICla
IClb
IClc
IC2a
IC2b
IC2c
IC3a
IC3b
IIA1
IIA2
IIA3
IIA4
IIBl
IIB2
IIC1
IIC2
IIC3
Sources
C&C, Item 34; COP, T. 24; COP, US_, T. 96
C&C. Item 37
COP, T.89; COP, US, T. 105
COP, US, T. 140 and 183
Same as IA4
See Health and Education Component IIBl
Same as IA4
COP, T. 83 and COP. US_, T. 99
Same as IB3a
See Environmental Component IC2
See Environmental Component IC3
C&C, Item 101
See Political Component IA1
See Political Component IA2
See Political Component IA3
COP, US_, Table 35
C&C, Items 12 and 14
C&C, Items 51 and 68; COP. T. 91; COP, US.,
Tables 75, 119, 183
COP. Tables 86, 91, 93 and COP. US, Tables
75, 91, 119, 183
COP, Tables 83, 85, 91, 92 and COP. US. Tables
75, 101, 119, 120
Same as IIA3
COP, Tables 83 and 85 and COP, US. Tables 75
and 101
COP, Tables 83 and 86 and COP, US, Tables 75
and 91
C&C, Item 49
See Economic Component IIEl
COP, Tables 81 and 91 and COP, US, Tables
Year
1970
1970
1969
1970
1970
1967
1970
1970
1970
1971
1971
1970
1971
1970
1974
1970
1970
1969
1970
1970
1970
1970
1970
1970
1969
1970
107 and 124
288
-------
IIIA1 COP, US_, Tables 141 and 184
IIIA2 C&C, Item 96
IIIA3 C&C, Item 91
IIIA4 C&C. Item 100
IIIA5 C&C, Item 48
IIIA6 U.S. Federal Bureau of Investigation, Uniform
Crime Reports for the United States, 1972
IIIA7 American Chamber of Commerce Researchers
Association, "Cost of Living Indicators"
IIIBla-d See Environmental IB3
IIIB2 SA, 1972, Section 33
IIIB3 C&C, Item 135
I1IB4 C&C, Item 15
IIIB5 See Health and Education Component IIA2
IIIB6 American Library Directory, 1970-1971
IIIC1 C&C, Item 22
IIIC2 C&C, Item 21
IIIC3 MRI Questionnaire
IIIC4 MRI Questionnaire
1970
1970
1970
1970
1970
1972
1970
1972
1972
1967
1967
1969 and 1970
1970
1969
1968
1970
1970
289
-------
Metropolitan Areas
Chambers of Commerce Questionnaire
Name of Respondent
Organization
Address
Title
Zip Code
Telephone No. Area Code ( )
1. Number of full-time employees on the staff of your Metropolitan Chamber of
Commerce in 1970
2. What is the dollar amount of the Chamber of Commerce budget in 1970 $
3. Please check the appropriate columns for those cultural events which were
held on a regular basis in the metropolitan area in 1970:
Class of Event
(check where applicable)
Event
Dance
Ballet
Modern
Folk/Ethnic
Drama
Plays
Stage Productions
Opera
Music
Symphonic/Philharmonic
Chamber Music Groups
Choirs
Country-Western-Bluegrass
Rock Concerts
Jazz
?rofessional
Semi-
Professional
University or
College
Touring
Groups
290
-------
4. Please indicate the number of the following cultural institutions located in the
metropolitan area in 1970:
Institutions
Number
Art Museums
Science Museums
History Museums
Natural Science Museums
5. Please indicate the size and scope of fairs and festivals held in the metropolitan
area in 1970:
Event
Fairs; (please list)
Festivals: (please list)
Local
Importance
Regional
Importance
National
Importance
6. Please check the appropriate columns for those sports events which were played
on a regular season basis in the metropolitan area in 1970:
Class of Team
Event
a. Football
b. Baseball
c. Basketball
d. Hockey
e. Soccer
yiajor
League
Minor
League
College or
University
291
-------
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Thorndike, E. L., 144 Smaller Cities (New York: Harcourt, Brace and
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Thorndike, E. L., Your City (New York: Harcourt, Brace and Company,
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305
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306
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Numbers refer to the pages in the content of the report.
AUTHOR INDEX
Addman, I., 91
Advisory Commission on Inter-
governmental Relation, 22
Allardt, E., 34
American Academy of Arts and
Sciences, 5, 14
American Statistical Association, 6
Anderson, J., 26
Arrow, K., 37
B
Bailey, J. P., 91
Ball, S. J., 25
Balsley, J., 18
Barrett, L., 63
Bauer, R., 3, 7, 208
Becker, G., 24
Behrens, W., 1, 6
Bell, W. H., 30
Berendt, J., 27, 29
Bergmann, B., 24
Berry, J. L., 65
Bier, D., 15
Borts, G. H., 99
Bradbum, N., 14, 16, 50
Bullard, J. L., 30
Campbell, A., 15, 225
Campbell, H. J., 15
Cantril A. H., 81, 93
Cantril, H., 10, 16, 120
Castle, E. N., 28, 39
Christakis, A., 11, 82
Christian, D. E., 33
Citizens Advisory Committee on
Environmental Quality, 119
Citizens Conference on State
Legislature, 20, 111
Cohn, W., 7
Cole, R., 6, 38
Conversee, P., 15
Coughlin, R., 30, 136
Council of Municipal Performance, 107
Crew, R, E., 91
Crittenden, J., Ill
D
Dalkey, N. C., 11, 80
Daniere, A., 127
Denison, E. F., 14, 127
Department of the Treasury of
New Jersey, 30
Ditton, R., 17
307
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Dorfman, N., 51, 152
Dorfman, R., 23, 51, 152
E
Easterlin, R. A., 43, 217
Easton, D., 19
Economic Planning Center, 34
Ehrlich, A. H., 63
Ehrlich, P. R., 63
Elgin, D., 31
Elmhorn, K., 34
Environmental Protection
Agency, 7, 8, 11, 18, 84, 93
H
Haber, A., 101
Hamilton, M., 24
Hanayama, Y. 34
Hanshaw, B., 18
Harman, H. H., 91
Hawley, A., 26
Herzberg, F., 16, 50
Hoel, P. G., 84
Hofferbert, R., 111
Holdren, J. P., 63
Homans, J., 25
Horn, P., 108
Huntley, W., 15
Paris, R. E., 25
Flaming, K. H., 30
Flax, M., 28, 30, 98, 107
Foster, R., 35
Francis, W., 22
Frank, D., 18
Freedman, H., 9
Forrester, J., 1
Galbraith, J. K., 1, 37
Garn, H., 28
Gautrin, J., 156
Gehrmann, F., 34
Goodale, T., 17
Governmental Statistics
Service, 34
Graves, C., 15
Grumm, J., 22
Guertin, W. H., 91
Inhaber, H., 18
Jacoby, H., 23
Japanese Economic Planning
Agency, 34
Jessen, R. J., 84
Johnson, G. E., 24
K
Kamrany, N. M., 82
Kanton, R., 28
Keynes, J. M., 1
Kneese, A., 112
Koelle, H. H., 34
Kunkel, J., 25
Kuznets, S., 13
Land, K. C., 9
Lave, L., 19, 113
308
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Lenski, G., 25
Leontief, W., 19, 27
Leopold, L., 18
Lineberry, R., 30
Liu, B. C., 4, 11, 26, 28, 100,
111, 210, 217
Logothetti, T., 28
Louis, A., 31, 97, 136
Lowenthal, D., 19
Lowry, M., 30
Lyle, J., 18
M
Macy, B., 35
Mandel, A., 30
Mansner, B., 16, 50
Marlin, J., 31
Marshall, H., 24
Maruo, N., 35
Maslow, A., 15, 40, 48, 128
Maxine, S., 7
McGinnies, E., 25
Mechling, J., 127
Meadows, D. H., 6
Meadows, D. L., 1, 6
Michalos, A., 35
Miller, R., 108
Mills, E. S., 151
Mishan, E. J., 6
Mitchell, A., 28
Moore, W., 7
Morris, C., 91
Myrdal, G., 99
N
Nagasawa, R., 25
National Advisory Commission on
Criminal Justice Standards and
Goals, 22
National Goals Research
Staff, 3
National Wildlife Federation, 81
Nordhaus, W., 6, 37
North, D., 108
0
Office for Planning and Programming,
Iowa, 30
Olson, M., 19
Ong, J. N., 30
Parker, S., 25
Parsons, T., 25
Patterson, S., 20
Pennings, J., 26
Perloff, H., 9, 11
President's Commission on
National Goals, 7
President's Council on Environ-
mental Quality, 111
President's Science Advisory
Committee, 208
Psacharopoulos, G., 127
Quinn, R., 35
R
Rander, J., 1, 6
Real Estate Research Company, 119
Robbins, L., 36
Rockefeller, J., 2
Rokeach, M., 25
Roll, C. W., 81, 93
Romans, J. T., 99
Rourke, N., 11
Ruggles, H., 28
Ruggles, N., 6, 38
Ruggles, R., 6, 28, 38
Rummel, R. J. , 23
Runyon, R. P., 101
Samuelson, P., 36, 39, 42
Sawhill, I. V., 9
Schlesinger, J., 22
Scott, E., 16, 40, 120, 128
Seashore, S., 35
309
-------
Seskin, E., 19, 113
Sharkansky, I., 111
Sheldon, E., 7, 9
Shelly, M., 15
Shelly, P., 15
Shepard, L., 35
Shoemaker, P., 30
Simon, H», 25
Skinner, B. F., 25
Smith, D. M., 9, 29, 82, 136
Snyderman, B., 16, 50
Springer, M., 28
Stafford, F. P., 24
Stein, J. L., 99
Stevenson, D. W., 30
Stith, R., 30
Stone, R., 32
W
Waddell, T., 63
Walker, J., Ill
Welch, F., 24
Whitman, I., 11, 18
Wilson, J., 27, 82, 91, 111
Wingo, L., 11, 28, 42
Wolf, C. P., 16
Wright, M. E., 40
Taeuber, R., 209
Taylor, J., 28
Terleckyz, N., 11, 28
Thomas, H., 19
Thorndike, E. L., 30
Tobin, J», 6, 37
Torres, J., 31
Tunstall, D. B., 8, 208
U
Ullman, E. L., 98
U.S. Department of Commerce, 127
U.S. Department of Health,
Education and Welfare, 8
U.S. Department of Housing and
Urban Development, 10
Urban Institute and International
City Management Association, 22
V
Van Dusen, R., 209
von Wodtke, M., 18
310
A U.S. GOVERNMENT PRINTING OFFICE: 1975-
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